charlieoneill/embedding-saes
Updated • 16
categories string | doi string | id string | year float64 | venue string | link string | updated string | published string | title string | abstract string | authors sequence |
|---|---|---|---|---|---|---|---|---|---|---|
null | null | 0001004 | null | null | http://arxiv.org/pdf/cs/0001004v1 | 2000-01-07T06:20:53Z | 2000-01-07T06:20:53Z | Multiplicative Algorithm for Orthgonal Groups and Independent Component
Analysis | The multiplicative Newton-like method developed by the author et al. is extended to the situation where the dynamics is restricted to the orthogonal group. A general framework is constructed without specifying the cost function. Though the restriction to the orthogonal groups makes the problem somewhat complicated, an ... | [
"['Toshinao Akuzawa']"
] |
null | null | 0001008 | null | null | http://arxiv.org/pdf/cs/0001008v3 | 2003-06-20T14:20:48Z | 2000-01-12T20:57:59Z | Predicting the expected behavior of agents that learn about agents: the
CLRI framework | We describe a framework and equations used to model and predict the behavior of multi-agent systems (MASs) with learning agents. A difference equation is used for calculating the progression of an agent's error in its decision function, thereby telling us how the agent is expected to fare in the MAS. The equation relie... | [
"['Jose M. Vidal' 'Edmund H. Durfee']"
] |
null | null | 0001027 | null | null | http://arxiv.org/pdf/cs/0001027v1 | 2000-01-29T01:23:54Z | 2000-01-29T01:23:54Z | Pattern Discovery and Computational Mechanics | Computational mechanics is a method for discovering, describing and quantifying patterns, using tools from statistical physics. It constructs optimal, minimal models of stochastic processes and their underlying causal structures. These models tell us about the intrinsic computation embedded within a process---how it st... | [
"['Cosma Rohilla Shalizi' 'James P. Crutchfield']"
] |
null | null | 0002006 | null | null | http://arxiv.org/abs/cs/0002006v1 | 2000-02-09T06:44:28Z | 2000-02-09T06:44:28Z | Multiplicative Nonholonomic/Newton -like Algorithm | We construct new algorithms from scratch, which use the fourth order cumulant of stochastic variables for the cost function. The multiplicative updating rule here constructed is natural from the homogeneous nature of the Lie group and has numerous merits for the rigorous treatment of the dynamics. As one consequence, t... | [
"['Toshinao Akuzawa' 'Noboru Murata']"
] |
null | null | 0003072 | null | null | http://arxiv.org/pdf/cs/0003072v1 | 2000-03-22T12:49:38Z | 2000-03-22T12:49:38Z | MOO: A Methodology for Online Optimization through Mining the Offline
Optimum | Ports, warehouses and courier services have to decide online how an arriving task is to be served in order that cost is minimized (or profit maximized). These operators have a wealth of historical data on task assignments; can these data be mined for knowledge or rules that can help the decision-making? MOO is a nove... | [
"['Jason W. H. Lee' 'Y. C. Tay' 'Anthony K. H. Tung']"
] |
null | null | 0004001 | null | null | http://arxiv.org/pdf/cs/0004001v1 | 2000-04-03T06:16:16Z | 2000-04-03T06:16:16Z | A Theory of Universal Artificial Intelligence based on Algorithmic
Complexity | Decision theory formally solves the problem of rational agents in uncertain worlds if the true environmental prior probability distribution is known. Solomonoff's theory of universal induction formally solves the problem of sequence prediction for unknown prior distribution. We combine both ideas and get a parameterles... | [
"['Marcus Hutter']"
] |
null | null | 0004057 | null | null | http://arxiv.org/pdf/physics/0004057v1 | 2000-04-24T15:22:30Z | 2000-04-24T15:22:30Z | The information bottleneck method | We define the relevant information in a signal $xin X$ as being the information that this signal provides about another signal $yin Y$. Examples include the information that face images provide about the names of the people portrayed, or the information that speech sounds provide about the words spoken. Understanding t... | [
"['Naftali Tishby' 'Fernando C. Pereira' 'William Bialek']"
] |
null | null | 0005021 | null | null | http://arxiv.org/pdf/cs/0005021v1 | 2000-05-14T14:35:20Z | 2000-05-14T14:35:20Z | Modeling the Uncertainty in Complex Engineering Systems | Existing procedures for model validation have been deemed inadequate for many engineering systems. The reason of this inadequacy is due to the high degree of complexity of the mechanisms that govern these systems. It is proposed in this paper to shift the attention from modeling the engineering system itself to modelin... | [
"['A. Guergachi']"
] |
null | null | 0005027 | null | null | http://arxiv.org/abs/cs/0005027v1 | 2000-05-26T20:24:48Z | 2000-05-26T20:24:48Z | A Bayesian Reflection on Surfaces | The topic of this paper is a novel Bayesian continuous-basis field representation and inference framework. Within this paper several problems are solved: The maximally informative inference of continuous-basis fields, that is where the basis for the field is itself a continuous object and not representable in a finite ... | [
"['David R. Wolf']"
] |
null | null | 0006025 | null | null | http://arxiv.org/abs/nlin/0006025v1 | 2000-06-16T17:01:39Z | 2000-06-16T17:01:39Z | Information Bottlenecks, Causal States, and Statistical Relevance Bases:
How to Represent Relevant Information in Memoryless Transduction | Discovering relevant, but possibly hidden, variables is a key step in constructing useful and predictive theories about the natural world. This brief note explains the connections between three approaches to this problem: the recently introduced information-bottleneck method, the computational mechanics approach to inf... | [
"['Cosma Rohilla Shalizi' 'James P. Crutchfield']"
] |
null | null | 0006233 | null | null | http://arxiv.org/pdf/math/0006233v3 | 2001-10-09T17:53:45Z | 2000-06-30T17:19:06Z | Algorithmic Statistics | While Kolmogorov complexity is the accepted absolute measure of information content of an individual finite object, a similarly absolute notion is needed for the relation between an individual data sample and an individual model summarizing the information in the data, for example, a finite set (or probability distribu... | [
"['Peter Gacs' 'John Tromp' 'Paul Vitanyi']"
] |
null | null | 0007026 | null | null | http://arxiv.org/pdf/cs/0007026v1 | 2000-07-14T00:33:12Z | 2000-07-14T00:33:12Z | Integrating E-Commerce and Data Mining: Architecture and Challenges | We show that the e-commerce domain can provide all the right ingredients for successful data mining and claim that it is a killer domain for data mining. We describe an integrated architecture, based on our expe-rience at Blue Martini Software, for supporting this integration. The architecture can dramatically reduce t... | [
"['Suhail Ansari' 'Ron Kohavi' 'Llew Mason' 'Zijian Zheng']"
] |
null | null | 0007070 | null | null | http://arxiv.org/pdf/physics/0007070v3 | 2001-01-23T20:02:27Z | 2000-07-20T00:45:11Z | Predictability, complexity and learning | We define {em predictive information} $I_{rm pred} (T)$ as the mutual information between the past and the future of a time series. Three qualitatively different behaviors are found in the limit of large observation times $T$: $I_{rm pred} (T)$ can remain finite, grow logarithmically, or grow as a fractional power law.... | [
"['William Bialek' 'Ilya Nemenman' 'Naftali Tishby']"
] |
null | null | 0008009 | null | null | http://arxiv.org/pdf/cs/0008009v1 | 2000-08-15T15:20:18Z | 2000-08-15T15:20:18Z | Data Mining to Measure and Improve the Success of Web Sites | For many companies, competitiveness in e-commerce requires a successful presence on the web. Web sites are used to establish the company's image, to promote and sell goods and to provide customer support. The success of a web site affects and reflects directly the success of the company in the electronic market. In thi... | [
"['Myra Spiliopoulou' 'Carsten Pohle']"
] |
null | null | 0008019 | null | null | http://arxiv.org/pdf/cs/0008019v1 | 2000-08-22T11:20:14Z | 2000-08-22T11:20:14Z | An Experimental Comparison of Naive Bayesian and Keyword-Based Anti-Spam
Filtering with Personal E-mail Messages | The growing problem of unsolicited bulk e-mail, also known as "spam", has generated a need for reliable anti-spam e-mail filters. Filters of this type have so far been based mostly on manually constructed keyword patterns. An alternative approach has recently been proposed, whereby a Naive Bayesian classifier is traine... | [
"['Ion Androutsopoulos' 'John Koutsias' 'Konstantinos V. Chandrinos'\n 'Constantine D. Spyropoulos']"
] |
null | null | 0008022 | null | null | http://arxiv.org/pdf/cs/0008022v1 | 2000-08-22T21:37:50Z | 2000-08-22T21:37:50Z | A Learning Approach to Shallow Parsing | A SNoW based learning approach to shallow parsing tasks is presented and studied experimentally. The approach learns to identify syntactic patterns by combining simple predictors to produce a coherent inference. Two instantiations of this approach are studied and experimental results for Noun-Phrases (NP) and Subject-V... | [
"['Marcia Muñoz' 'Vasin Punyakanok' 'Dan Roth' 'Dav Zimak']"
] |
null | null | 0009001 | null | null | http://arxiv.org/pdf/cs/0009001v3 | 2002-02-26T01:51:09Z | 2000-09-05T18:54:58Z | Complexity analysis for algorithmically simple strings | Given a reference computer, Kolmogorov complexity is a well defined function on all binary strings. In the standard approach, however, only the asymptotic properties of such functions are considered because they do not depend on the reference computer. We argue that this approach can be more useful if it is refined to ... | [
"['Andrei N. Soklakov']"
] |
null | null | 0009007 | null | null | http://arxiv.org/pdf/cs/0009007v1 | 2000-09-13T21:09:47Z | 2000-09-13T21:09:47Z | Robust Classification for Imprecise Environments | In real-world environments it usually is difficult to specify target operating conditions precisely, for example, target misclassification costs. This uncertainty makes building robust classification systems problematic. We show that it is possible to build a hybrid classifier that will perform at least as well as the ... | [
"['Foster Provost' 'Tom Fawcett']"
] |
null | null | 0009009 | null | null | http://arxiv.org/pdf/cs/0009009v1 | 2000-09-18T14:05:13Z | 2000-09-18T14:05:13Z | Learning to Filter Spam E-Mail: A Comparison of a Naive Bayesian and a
Memory-Based Approach | We investigate the performance of two machine learning algorithms in the context of anti-spam filtering. The increasing volume of unsolicited bulk e-mail (spam) has generated a need for reliable anti-spam filters. Filters of this type have so far been based mostly on keyword patterns that are constructed by hand and pe... | [
"['Ion Androutsopoulos' 'Georgios Paliouras' 'Vangelis Karkaletsis'\n 'Georgios Sakkis' 'Constantine D. Spyropoulos' 'Panagiotis Stamatopoulos']"
] |
null | null | 0009027 | null | null | http://arxiv.org/pdf/cs/0009027v1 | 2000-09-28T14:25:51Z | 2000-09-28T14:25:51Z | A Classification Approach to Word Prediction | The eventual goal of a language model is to accurately predict the value of a missing word given its context. We present an approach to word prediction that is based on learning a representation for each word as a function of words and linguistics predicates in its context. This approach raises a few new questions that... | [
"['Yair Even-Zohar' 'Dan Roth']"
] |
null | null | 0009032 | null | null | http://arxiv.org/pdf/physics/0009032v1 | 2000-09-08T23:30:26Z | 2000-09-08T23:30:26Z | Information theory and learning: a physical approach | We try to establish a unified information theoretic approach to learning and to explore some of its applications. First, we define {em predictive information} as the mutual information between the past and the future of a time series, discuss its behavior as a function of the length of the series, and explain how other... | [
"['Ilya Nemenman']"
] |
null | null | 0009165 | null | null | http://arxiv.org/abs/cond-mat/0009165v2 | 2002-02-05T00:04:38Z | 2000-09-11T22:51:53Z | Occam factors and model-independent Bayesian learning of continuous
distributions | Learning of a smooth but nonparametric probability density can be regularized using methods of Quantum Field Theory. We implement a field theoretic prior numerically, test its efficacy, and show that the data and the phase space factors arising from the integration over the model space determine the free parameter of t... | [
"['Ilya Nemenman' 'William Bialek']"
] |
null | null | 0010001 | null | null | http://arxiv.org/pdf/cs/0010001v1 | 2000-09-30T11:47:42Z | 2000-09-30T11:47:42Z | Design of an Electro-Hydraulic System Using Neuro-Fuzzy Techniques | Increasing demands in performance and quality make drive systems fundamental parts in the progressive automation of industrial processes. Their conventional models become inappropriate and have limited scope if one requires a precise and fast performance. So, it is important to incorporate learning capabilities into dr... | [
"['P. J. Costa Branco' 'J. A. Dente']"
] |
null | null | 0010002 | null | null | http://arxiv.org/pdf/cs/0010002v1 | 2000-09-30T14:37:23Z | 2000-09-30T14:37:23Z | Noise Effects in Fuzzy Modelling Systems | Noise is source of ambiguity for fuzzy systems. Although being an important aspect, the effects of noise in fuzzy modeling have been little investigated. This paper presents a set of tests using three well-known fuzzy modeling algorithms. These evaluate perturbations in the extracted rule-bases caused by noise pollutin... | [
"['P. J. Costa Branco' 'J. A. Dente']"
] |
null | null | 0010003 | null | null | http://arxiv.org/abs/cs/0010003v1 | 2000-09-30T15:31:16Z | 2000-09-30T15:31:16Z | Torque Ripple Minimization in a Switched Reluctance Drive by Neuro-Fuzzy
Compensation | Simple power electronic drive circuit and fault tolerance of converter are specific advantages of SRM drives, but excessive torque ripple has limited its use to special applications. It is well known that controlling the current shape adequately can minimize the torque ripple. This paper presents a new method for shapi... | [
"['L. Henriques' 'L. Rolim' 'W. Suemitsu' 'P. J. Costa Branco'\n 'J. A. Dente']"
] |
null | null | 0010004 | null | null | http://arxiv.org/pdf/cs/0010004v1 | 2000-09-30T15:42:55Z | 2000-09-30T15:42:55Z | A Fuzzy Relational Identification Algorithm and Its Application to
Predict The Behaviour of a Motor Drive System | Fuzzy relational identification builds a relational model describing systems behaviour by a nonlinear mapping between its variables. In this paper, we propose a new fuzzy relational algorithm based on simplified max-min relational equation. The algorithm presents an adaptation method applied to gravity-center of each f... | [
"['P. J. Costa Branco' 'J. A. Dente']"
] |
null | null | 0010006 | null | null | http://arxiv.org/pdf/cs/0010006v1 | 2000-10-02T12:16:17Z | 2000-10-02T12:16:17Z | Applications of Data Mining to Electronic Commerce | Electronic commerce is emerging as the killer domain for data mining technology. The following are five desiderata for success. Seldom are they they all present in one data mining application. 1. Data with rich descriptions. For example, wide customer records with many potentially useful fields allow data mining al... | [
"['Ron Kohavi' 'Foster Provost']"
] |
null | null | 0010010 | null | null | http://arxiv.org/abs/cs/0010010v1 | 2000-10-03T17:54:38Z | 2000-10-03T17:54:38Z | Fault Detection using Immune-Based Systems and Formal Language
Algorithms | This paper describes two approaches for fault detection: an immune-based mechanism and a formal language algorithm. The first one is based on the feature of immune systems in distinguish any foreign cell from the body own cell. The formal language approach assumes the system as a linguistic source capable of generating... | [
"['J. F. Martins' 'P. J. Costa Branco' 'A. J. Pires' 'J. A. Dente']"
] |
null | null | 0010022 | null | null | http://arxiv.org/pdf/cs/0010022v1 | 2000-10-15T20:14:08Z | 2000-10-15T20:14:08Z | Noise-Tolerant Learning, the Parity Problem, and the Statistical Query
Model | We describe a slightly sub-exponential time algorithm for learning parity functions in the presence of random classification noise. This results in a polynomial-time algorithm for the case of parity functions that depend on only the first O(log n log log n) bits of input. This is the first known instance of an efficien... | [
"['Avrim Blum' 'Adam Kalai' 'Hal Wasserman']"
] |
null | null | 0011032 | null | null | http://arxiv.org/pdf/cs/0011032v1 | 2000-11-21T21:51:01Z | 2000-11-21T21:51:01Z | Top-down induction of clustering trees | An approach to clustering is presented that adapts the basic top-down induction of decision trees method towards clustering. To this aim, it employs the principles of instance based learning. The resulting methodology is implemented in the TIC (Top down Induction of Clustering trees) system for first order clustering. ... | [
"['Hendrik Blockeel' 'Luc De Raedt' 'Jan Ramon']"
] |
null | null | 0011033 | null | null | http://arxiv.org/pdf/cs/0011033v1 | 2000-11-22T09:41:53Z | 2000-11-22T09:41:53Z | Web Mining Research: A Survey | With the huge amount of information available online, the World Wide Web is a fertile area for data mining research. The Web mining research is at the cross road of research from several research communities, such as database, information retrieval, and within AI, especially the sub-areas of machine learning and natura... | [
"['Raymond Kosala' 'Hendrik Blockeel']"
] |
null | null | 0011038 | null | null | http://arxiv.org/pdf/cs/0011038v1 | 2000-11-23T14:48:53Z | 2000-11-23T14:48:53Z | Provably Fast and Accurate Recovery of Evolutionary Trees through
Harmonic Greedy Triplets | We give a greedy learning algorithm for reconstructing an evolutionary tree based on a certain harmonic average on triplets of terminal taxa. After the pairwise distances between terminal taxa are estimated from sequence data, the algorithm runs in O(n^2) time using O(n) work space, where n is the number of terminal ta... | [
"['Miklos Csuros' 'Ming-Yang Kao']"
] |
null | null | 0011044 | null | null | http://arxiv.org/pdf/cs/0011044v1 | 2000-11-29T12:14:50Z | 2000-11-29T12:14:50Z | Scaling Up Inductive Logic Programming by Learning from Interpretations | When comparing inductive logic programming (ILP) and attribute-value learning techniques, there is a trade-off between expressive power and efficiency. Inductive logic programming techniques are typically more expressive but also less efficient. Therefore, the data sets handled by current inductive logic programming sy... | [
"['Hendrik Blockeel' 'Luc De Raedt' 'Nico Jacobs' 'Bart Demoen']"
] |
null | null | 0011122 | null | null | http://arxiv.org/pdf/quant-ph/0011122v2 | 2000-12-20T14:54:39Z | 2000-11-30T14:23:55Z | Algorithmic Theories of Everything | The probability distribution P from which the history of our universe is sampled represents a theory of everything or TOE. We assume P is formally describable. Since most (uncountably many) distributions are not, this imposes a strong inductive bias. We show that P(x) is small for any universe x lacking a short descrip... | [
"['Juergen Schmidhuber']"
] |
null | null | 0012011 | null | null | http://arxiv.org/pdf/cs/0012011v1 | 2000-12-16T09:38:13Z | 2000-12-16T09:38:13Z | Towards a Universal Theory of Artificial Intelligence based on
Algorithmic Probability and Sequential Decision Theory | Decision theory formally solves the problem of rational agents in uncertain worlds if the true environmental probability distribution is known. Solomonoff's theory of universal induction formally solves the problem of sequence prediction for unknown distribution. We unify both theories and give strong arguments that th... | [
"['Marcus Hutter']"
] |
null | null | 0012163 | null | null | http://arxiv.org/pdf/math/0012163v2 | 2000-12-19T07:17:10Z | 2000-12-18T10:35:00Z | Learning Complexity Dimensions for a Continuous-Time Control System | This paper takes a computational learning theory approach to a problem of linear systems identification. It is assumed that input signals have only a finite number k of frequency components, and systems to be identified have dimension no greater than n. The main result establishes that the sample complexity needed for ... | [
"['Pirkko Kuusela' 'Daniel Ocone' 'Eduardo D. Sontag']"
] |
null | null | 0101019 | null | null | http://arxiv.org/pdf/cs/0101019v2 | 2001-09-19T09:12:56Z | 2001-01-21T17:19:37Z | General Loss Bounds for Universal Sequence Prediction | The Bayesian framework is ideally suited for induction problems. The probability of observing $x_t$ at time $t$, given past observations $x_1...x_{t-1}$ can be computed with Bayes' rule if the true distribution $mu$ of the sequences $x_1x_2x_3...$ is known. The problem, however, is that in many cases one does not even ... | [
"['Marcus Hutter']"
] |
null | null | 0102015 | null | null | http://arxiv.org/pdf/cs/0102015v1 | 2001-02-20T13:08:15Z | 2001-02-20T13:08:15Z | Non-convex cost functionals in boosting algorithms and methods for panel
selection | In this document we propose a new improvement for boosting techniques as proposed in Friedman '99 by the use of non-convex cost functional. The idea is to introduce a correlation term to better deal with forecasting of additive time series. The problem is discussed in a theoretical way to prove the existence of minimiz... | [
"['Marco Visentin']"
] |
null | null | 0102018 | null | null | http://arxiv.org/pdf/cs/0102018v1 | 2001-02-21T20:52:28Z | 2001-02-21T20:52:28Z | An effective Procedure for Speeding up Algorithms | The provably asymptotically fastest algorithm within a factor of 5 for formally described problems will be constructed. The main idea is to enumerate all programs provably equivalent to the original problem by enumerating all proofs. The algorithm could be interpreted as a generalization and improvement of Levin search... | [
"['Marcus Hutter']"
] |
null | null | 0103003 | null | null | http://arxiv.org/pdf/cs/0103003v1 | 2001-03-02T01:55:46Z | 2001-03-02T01:55:46Z | Learning Policies with External Memory | In order for an agent to perform well in partially observable domains, it is usually necessary for actions to depend on the history of observations. In this paper, we explore a {it stigmergic} approach, in which the agent's actions include the ability to set and clear bits in an external memory, and the external memory... | [
"['Leonid Peshkin' 'Nicolas Meuleau' 'Leslie Kaelbling']"
] |
null | null | 0103015 | null | null | http://arxiv.org/pdf/cs/0103015v1 | 2001-03-14T18:40:32Z | 2001-03-14T18:40:32Z | Fitness Uniform Selection to Preserve Genetic Diversity | In evolutionary algorithms, the fitness of a population increases with time by mutating and recombining individuals and by a biased selection of more fit individuals. The right selection pressure is critical in ensuring sufficient optimization progress on the one hand and in preserving genetic diversity to be able to e... | [
"['Marcus Hutter']"
] |
null | null | 0104005 | null | null | http://arxiv.org/pdf/cs/0104005v1 | 2001-04-03T14:09:12Z | 2001-04-03T14:09:12Z | Bootstrapping Structure using Similarity | In this paper a new similarity-based learning algorithm, inspired by string edit-distance (Wagner and Fischer, 1974), is applied to the problem of bootstrapping structure from scratch. The algorithm takes a corpus of unannotated sentences as input and returns a corpus of bracketed sentences. The method works on pairs o... | [
"['Menno van Zaanen']"
] |
null | null | 0104006 | null | null | http://arxiv.org/pdf/cs/0104006v1 | 2001-04-03T14:20:26Z | 2001-04-03T14:20:26Z | ABL: Alignment-Based Learning | This paper introduces a new type of grammar learning algorithm, inspired by string edit distance (Wagner and Fischer, 1974). The algorithm takes a corpus of flat sentences as input and returns a corpus of labelled, bracketed sentences. The method works on pairs of unstructured sentences that have one or more words in c... | [
"['Menno van Zaanen']"
] |
null | null | 0104007 | null | null | http://arxiv.org/pdf/cs/0104007v1 | 2001-04-03T15:03:16Z | 2001-04-03T15:03:16Z | Bootstrapping Syntax and Recursion using Alignment-Based Learning | This paper introduces a new type of unsupervised learning algorithm, based on the alignment of sentences and Harris's (1951) notion of interchangeability. The algorithm is applied to an untagged, unstructured corpus of natural language sentences, resulting in a labelled, bracketed version of the corpus. Firstly, the al... | [
"['Menno van Zaanen']"
] |
null | null | 0105025 | null | null | http://arxiv.org/pdf/cs/0105025v1 | 2001-05-15T19:07:28Z | 2001-05-15T19:07:28Z | Market-Based Reinforcement Learning in Partially Observable Worlds | Unlike traditional reinforcement learning (RL), market-based RL is in principle applicable to worlds described by partially observable Markov Decision Processes (POMDPs), where an agent needs to learn short-term memories of relevant previous events in order to execute optimal actions. Most previous work, however, has f... | [
"['Ivo Kwee' 'Marcus Hutter' 'Juergen Schmidhuber']"
] |
null | null | 0105027 | null | null | http://arxiv.org/pdf/cs/0105027v1 | 2001-05-17T18:33:56Z | 2001-05-17T18:33:56Z | Bounds on sample size for policy evaluation in Markov environments | Reinforcement learning means finding the optimal course of action in Markovian environments without knowledge of the environment's dynamics. Stochastic optimization algorithms used in the field rely on estimates of the value of a policy. Typically, the value of a policy is estimated from results of simulating that very... | [
"['Leonid Peshkin' 'Sayan Mukherjee']"
] |
null | null | 0105032 | null | null | http://arxiv.org/pdf/cs/0105032v1 | 2001-05-25T02:52:07Z | 2001-05-25T02:52:07Z | Learning to Cooperate via Policy Search | Cooperative games are those in which both agents share the same payoff structure. Value-based reinforcement-learning algorithms, such as variants of Q-learning, have been applied to learning cooperative games, but they only apply when the game state is completely observable to both agents. Policy search methods are a r... | [
"['Leonid Peshkin' 'Kee-Eung Kim' 'Nicolas Meuleau' 'Leslie Pack Kaelbling']"
] |
null | null | 0105235 | null | null | http://arxiv.org/pdf/math/0105235v3 | 2001-12-03T02:25:00Z | 2001-05-29T02:20:17Z | Mathematics of learning | We study the convergence properties of a pair of learning algorithms (learning with and without memory). This leads us to study the dominant eigenvalue of a class of random matrices. This turns out to be related to the roots of the derivative of random polynomials (generated by picking their roots uniformly at random i... | [
"['Natalia Komarova' 'Igor Rivin']"
] |
null | null | 0105236 | null | null | http://arxiv.org/pdf/math/0105236v2 | 2001-12-03T02:22:12Z | 2001-05-29T02:25:23Z | Harmonic mean, random polynomials and stochastic matrices | Motivated by a problem in learning theory, we are led to study the dominant eigenvalue of a class of random matrices. This turns out to be related to the roots of the derivative of random polynomials (generated by picking their roots uniformly at random in the interval [0, 1], although our results extend to other distr... | [
"['Natalia Komarova' 'Igor Rivin']"
] |
null | null | 0106016 | null | null | http://arxiv.org/pdf/cs/0106016v1 | 2001-06-10T14:56:51Z | 2001-06-10T14:56:51Z | File mapping Rule-based DBMS and Natural Language Processing | This paper describes the system of storage, extract and processing of information structured similarly to the natural language. For recursive inference the system uses the rules having the same representation, as the data. The environment of storage of information is provided with the File Mapping (SHM) mechanism of op... | [
"['Vjacheslav M. Novikov']"
] |
null | null | 0106036 | null | null | http://arxiv.org/pdf/cs/0106036v1 | 2001-06-15T09:12:51Z | 2001-06-15T09:12:51Z | Convergence and Error Bounds for Universal Prediction of Nonbinary
Sequences | Solomonoff's uncomputable universal prediction scheme $xi$ allows to predict the next symbol $x_k$ of a sequence $x_1...x_{k-1}$ for any Turing computable, but otherwise unknown, probabilistic environment $mu$. This scheme will be generalized to arbitrary environmental classes, which, among others, allows the construct... | [
"['Marcus Hutter']"
] |
null | null | 0106044 | null | null | http://arxiv.org/pdf/cs/0106044v1 | 2001-06-20T19:01:41Z | 2001-06-20T19:01:41Z | A Sequential Model for Multi-Class Classification | Many classification problems require decisions among a large number of competing classes. These tasks, however, are not handled well by general purpose learning methods and are usually addressed in an ad-hoc fashion. We suggest a general approach -- a sequential learning model that utilizes classifiers to sequentially ... | [
"['Yair Even-Zohar' 'Dan Roth']"
] |
null | null | 0107032 | null | null | http://arxiv.org/pdf/cs/0107032v1 | 2001-07-23T11:06:45Z | 2001-07-23T11:06:45Z | Coupled Clustering: a Method for Detecting Structural Correspondence | This paper proposes a new paradigm and computational framework for identification of correspondences between sub-structures of distinct composite systems. For this, we define and investigate a variant of traditional data clustering, termed coupled clustering, which simultaneously identifies corresponding clusters withi... | [
"['Zvika Marx' 'Ido Dagan' 'Joachim Buhmann']"
] |
null | null | 0107033 | null | null | http://arxiv.org/pdf/cs/0107033v1 | 2001-07-25T15:50:43Z | 2001-07-25T15:50:43Z | Yet another zeta function and learning | We study the convergence speed of the batch learning algorithm, and compare its speed to that of the memoryless learning algorithm and of learning with memory (as analyzed in joint work with N. Komarova). We obtain precise results and show in particular that the batch learning algorithm is never worse than the memoryle... | [
"['Igor Rivin']"
] |
null | null | 0108018 | null | null | http://arxiv.org/pdf/cs/0108018v1 | 2001-08-27T13:07:44Z | 2001-08-27T13:07:44Z | Bipartite graph partitioning and data clustering | Many data types arising from data mining applications can be modeled as bipartite graphs, examples include terms and documents in a text corpus, customers and purchasing items in market basket analysis and reviewers and movies in a movie recommender system. In this paper, we propose a new data clustering method based o... | [
"['H. Zha' 'X. He' 'C. Ding' 'M. Gu' 'H. Simon']"
] |
null | null | 0109034 | null | null | http://arxiv.org/pdf/cs/0109034v1 | 2001-09-19T08:07:38Z | 2001-09-19T08:07:38Z | Relevant Knowledge First - Reinforcement Learning and Forgetting in
Knowledge Based Configuration | In order to solve complex configuration tasks in technical domains, various knowledge based methods have been developed. However their applicability is often unsuccessful due to their low efficiency. One of the reasons for this is that (parts of the) problems have to be solved again and again, instead of being "learnt"... | [
"['Ingo Kreuz' 'Dieter Roller']"
] |
null | null | 0110036 | null | null | http://arxiv.org/pdf/cs/0110036v1 | 2001-10-17T15:45:23Z | 2001-10-17T15:45:23Z | Efficient algorithms for decision tree cross-validation | Cross-validation is a useful and generally applicable technique often employed in machine learning, including decision tree induction. An important disadvantage of straightforward implementation of the technique is its computational overhead. In this paper we show that, for decision trees, the computational overhead of... | [
"['Hendrik Blockeel' 'Jan Struyf']"
] |
null | null | 0110053 | null | null | http://arxiv.org/abs/cs/0110053v1 | 2001-10-26T09:27:48Z | 2001-10-26T09:27:48Z | Machine Learning in Automated Text Categorization | The automated categorization (or classification) of texts into predefined categories has witnessed a booming interest in the last ten years, due to the increased availability of documents in digital form and the ensuing need to organize them. In the research community the dominant approach to this problem is based on m... | [
"['Fabrizio Sebastiani']"
] |
null | null | 0111003 | null | null | http://arxiv.org/pdf/cs/0111003v1 | 2001-11-01T03:02:19Z | 2001-11-01T03:02:19Z | The Use of Classifiers in Sequential Inference | We study the problem of combining the outcomes of several different classifiers in a way that provides a coherent inference that satisfies some constraints. In particular, we develop two general approaches for an important subproblem-identifying phrase structure. The first is a Markovian approach that extends standard ... | [
"['Vasin Punyakanok' 'Dan Roth']"
] |
null | null | 0201005 | null | null | http://arxiv.org/pdf/cs/0201005v2 | 2002-10-10T17:23:57Z | 2002-01-08T16:44:10Z | Sharpening Occam's Razor | We provide a new representation-independent formulation of Occam's razor theorem, based on Kolmogorov complexity. This new formulation allows us to: (i) Obtain better sample complexity than both length-based and VC-based versions of Occam's razor theorem, in many applications. (ii) Achieve a sharper reverse of Occa... | [
"['Ming Li' 'John Tromp' 'Paul Vitanyi']"
] |
null | null | 0201009 | null | null | http://arxiv.org/pdf/cs/0201009v1 | 2002-01-14T18:38:55Z | 2002-01-14T18:38:55Z | The performance of the batch learner algorithm | We analyze completely the convergence speed of the emph{batch learning algorithm}, and compare its speed to that of the memoryless learning algorithm and of learning with memory. We show that the batch learning algorithm is never worse than the memoryless learning algorithm (at least asymptotically). Its performance em... | [
"['Igor Rivin']"
] |
null | null | 0201014 | null | null | http://arxiv.org/pdf/cs/0201014v1 | 2002-01-17T13:42:23Z | 2002-01-17T13:42:23Z | The Dynamics of AdaBoost Weights Tells You What's Hard to Classify | The dynamical evolution of weights in the Adaboost algorithm contains useful information about the role that the associated data points play in the built of the Adaboost model. In particular, the dynamics induces a bipartition of the data set into two (easy/hard) classes. Easy points are ininfluential in the making of ... | [
"['Bruno Caprile' 'Cesare Furlanello' 'Stefano Merler']"
] |
null | null | 0201021 | null | null | http://arxiv.org/pdf/cs/0201021v1 | 2002-01-23T11:58:17Z | 2002-01-23T11:58:17Z | Learning to Play Games in Extensive Form by Valuation | A valuation for a player in a game in extensive form is an assignment of numeric values to the players moves. The valuation reflects the desirability moves. We assume a myopic player, who chooses a move with the highest valuation. Valuations can also be revised, and hopefully improved, after each play of the game. Here... | [
"['Philippe Jehiel' 'Dov Samet']"
] |
null | null | 0202383 | null | null | http://arxiv.org/pdf/cond-mat/0202383v1 | 2002-02-21T18:25:29Z | 2002-02-21T18:25:29Z | Extended Comment on Language Trees and Zipping | This is the extended version of a Comment submitted to Physical Review Letters. I first point out the inappropriateness of publishing a Letter unrelated to physics. Next, I give experimental results showing that the technique used in the Letter is 3 times worse and 17 times slower than a simple baseline. And finally, I... | [
"['Joshua Goodman']"
] |
null | null | 0203010 | null | null | http://arxiv.org/pdf/cs/0203010v1 | 2002-03-07T10:16:25Z | 2002-03-07T10:16:25Z | On Learning by Exchanging Advice | One of the main questions concerning learning in Multi-Agent Systems is: (How) can agents benefit from mutual interaction during the learning process?. This paper describes the study of an interactive advice-exchange mechanism as a possible way to improve agents' learning performance. The advice-exchange technique, dis... | [
"['L. Nunes' 'E. Oliveira']"
] |
null | null | 0203011 | null | null | http://arxiv.org/pdf/cs/0203011v1 | 2002-03-08T15:58:23Z | 2002-03-08T15:58:23Z | Capturing Knowledge of User Preferences: ontologies on recommender
systems | Tools for filtering the World Wide Web exist, but they are hampered by the difficulty of capturing user preferences in such a dynamic environment. We explore the acquisition of user profiles by unobtrusive monitoring of browsing behaviour and application of supervised machine-learning techniques coupled with an ontolog... | [
"['S. E. Middleton' 'D. C. De Roure' 'N. R. Shadbolt']"
] |
null | null | 0203012 | null | null | http://arxiv.org/pdf/cs/0203012v1 | 2002-03-09T01:28:33Z | 2002-03-09T01:28:33Z | Interface agents: A review of the field | This paper reviews the origins of interface agents, discusses challenges that exist within the interface agent field and presents a survey of current attempts to find solutions to these challenges. A history of agent systems from their birth in the 1960's to the current day is described, along with the issues they try ... | [
"['Stuart E. Middleton']"
] |
null | null | 0204012 | null | null | http://arxiv.org/pdf/cs/0204012v1 | 2002-04-08T10:56:26Z | 2002-04-08T10:56:26Z | Exploiting Synergy Between Ontologies and Recommender Systems | Recommender systems learn about user preferences over time, automatically finding things of similar interest. This reduces the burden of creating explicit queries. Recommender systems do, however, suffer from cold-start problems where no initial information is available early on upon which to base recommendations. Sema... | [
"['Stuart E. Middleton' 'Harith Alani' 'David C. De Roure']"
] |
null | null | 0204040 | null | null | http://arxiv.org/pdf/cs/0204040v1 | 2002-04-17T10:46:00Z | 2002-04-17T10:46:00Z | Self-Optimizing and Pareto-Optimal Policies in General Environments
based on Bayes-Mixtures | The problem of making sequential decisions in unknown probabilistic environments is studied. In cycle $t$ action $y_t$ results in perception $x_t$ and reward $r_t$, where all quantities in general may depend on the complete history. The perception $x_t$ and reward $r_t$ are sampled from the (reactive) environmental pro... | [
"['Marcus Hutter']"
] |
null | null | 0204043 | null | null | http://arxiv.org/pdf/cs/0204043v1 | 2002-04-20T05:02:53Z | 2002-04-20T05:02:53Z | Learning from Scarce Experience | Searching the space of policies directly for the optimal policy has been one popular method for solving partially observable reinforcement learning problems. Typically, with each change of the target policy, its value is estimated from the results of following that very policy. This requires a large number of interacti... | [
"['Leonid Peshkin' 'Christian R. Shelton']"
] |
null | null | 0204052 | null | null | http://arxiv.org/pdf/cs/0204052v1 | 2002-04-26T14:33:29Z | 2002-04-26T14:33:29Z | Required sample size for learning sparse Bayesian networks with many
variables | Learning joint probability distributions on n random variables requires exponential sample size in the generic case. Here we consider the case that a temporal (or causal) order of the variables is known and that the (unknown) graph of causal dependencies has bounded in-degree Delta. Then the joint measure is uniquely d... | [
"['Pawel Wocjan' 'Dominik Janzing' 'Thomas Beth']"
] |
null | null | 0205025 | null | null | http://arxiv.org/pdf/cs/0205025v1 | 2002-05-16T12:35:00Z | 2002-05-16T12:35:00Z | Bootstrapping Structure into Language: Alignment-Based Learning | This thesis introduces a new unsupervised learning framework, called Alignment-Based Learning, which is based on the alignment of sentences and Harris's (1951) notion of substitutability. Instances of the framework can be applied to an untagged, unstructured corpus of natural language sentences, resulting in a labelled... | [
"['Menno M. van Zaanen']"
] |
null | null | 0205070 | null | null | http://arxiv.org/pdf/cs/0205070v1 | 2002-05-28T02:01:55Z | 2002-05-28T02:01:55Z | Thumbs up? Sentiment Classification using Machine Learning Techniques | We consider the problem of classifying documents not by topic, but by overall sentiment, e.g., determining whether a review is positive or negative. Using movie reviews as data, we find that standard machine learning techniques definitively outperform human-produced baselines. However, the three machine learning method... | [
"['Bo Pang' 'Lillian Lee' 'Shivakumar Vaithyanathan']"
] |
null | null | 0205072 | null | null | http://arxiv.org/pdf/cs/0205072v1 | 2002-05-29T17:48:48Z | 2002-05-29T17:48:48Z | Unsupervised Learning of Morphology without Morphemes | The first morphological learner based upon the theory of Whole Word Morphology Ford et al. (1997) is outlined, and preliminary evaluation results are presented. The program, Whole Word Morphologizer, takes a POS-tagged lexicon as input, induces morphological relationships without attempting to discover or identify morp... | [
"['Sylvain Neuvel' 'Sean A. Fulop']"
] |
null | null | 0206006 | null | null | http://arxiv.org/pdf/cs/0206006v1 | 2002-06-03T16:00:55Z | 2002-06-03T16:00:55Z | Robust Feature Selection by Mutual Information Distributions | Mutual information is widely used in artificial intelligence, in a descriptive way, to measure the stochastic dependence of discrete random variables. In order to address questions such as the reliability of the empirical value, one must consider sample-to-population inferential approaches. This paper deals with the di... | [
"['Marco Zaffalon' 'Marcus Hutter']"
] |
null | null | 0206017 | null | null | http://arxiv.org/pdf/cs/0206017v1 | 2002-06-10T16:02:36Z | 2002-06-10T16:02:36Z | The Prioritized Inductive Logic Programs | The limit behavior of inductive logic programs has not been explored, but when considering incremental or online inductive learning algorithms which usually run ongoingly, such behavior of the programs should be taken into account. An example is given to show that some inductive learning algorithm may not be correct in... | [
"['Shilong Ma' 'Yuefei Sui' 'Ke Xu']"
] |
null | null | 0207097 | null | null | http://arxiv.org/pdf/cs/0207097v2 | 2002-12-23T14:11:16Z | 2002-07-31T14:33:11Z | Optimal Ordered Problem Solver | We present a novel, general, optimally fast, incremental way of searching for a universal algorithm that solves each task in a sequence of tasks. The Optimal Ordered Problem Solver (OOPS) continually organizes and exploits previously found solutions to earlier tasks, efficiently searching not only the space of domain-s... | [
"['Juergen Schmidhuber']"
] |
null | null | 0210025 | null | null | http://arxiv.org/pdf/cs/0210025v3 | 2002-11-27T00:56:43Z | 2002-10-29T00:33:26Z | An Algorithm for Pattern Discovery in Time Series | We present a new algorithm for discovering patterns in time series and other sequential data. We exhibit a reliable procedure for building the minimal set of hidden, Markovian states that is statistically capable of producing the behavior exhibited in the data -- the underlying process's causal states. Unlike conventio... | [
"['Cosma Rohilla Shalizi' 'Kristina Lisa Shalizi' 'James P. Crutchfield']"
] |
null | null | 0211003 | null | null | http://arxiv.org/pdf/cs/0211003v1 | 2002-11-01T18:09:56Z | 2002-11-01T18:09:56Z | Evaluation of the Performance of the Markov Blanket Bayesian Classifier
Algorithm | The Markov Blanket Bayesian Classifier is a recently-proposed algorithm for construction of probabilistic classifiers. This paper presents an empirical comparison of the MBBC algorithm with three other Bayesian classifiers: Naive Bayes, Tree-Augmented Naive Bayes and a general Bayesian network. All of these are impleme... | [
"['Michael G. Madden']"
] |
null | null | 0211006 | null | null | http://arxiv.org/pdf/cs/0211006v1 | 2002-11-07T06:44:54Z | 2002-11-07T06:44:54Z | Maximing the Margin in the Input Space | We propose a novel criterion for support vector machine learning: maximizing the margin in the input space, not in the feature (Hilbert) space. This criterion is a discriminative version of the principal curve proposed by Hastie et al. The criterion is appropriate in particular when the input space is already a well-de... | [
"['Shotaro Akaho']"
] |
null | null | 0211007 | null | null | http://arxiv.org/pdf/cs/0211007v1 | 2002-11-07T07:21:58Z | 2002-11-07T07:21:58Z | Approximating Incomplete Kernel Matrices by the em Algorithm | In biological data, it is often the case that observed data are available only for a subset of samples. When a kernel matrix is derived from such data, we have to leave the entries for unavailable samples as missing. In this paper, we make use of a parametric model of kernel matrices, and estimate missing entries by fi... | [
"['Koji Tsuda' 'Shotaro Akaho' 'Kiyoshi Asai']"
] |
null | null | 0212008 | null | null | http://arxiv.org/pdf/cs/0212008v1 | 2002-12-07T18:51:12Z | 2002-12-07T18:51:12Z | Principal Manifolds and Nonlinear Dimension Reduction via Local Tangent
Space Alignment | Nonlinear manifold learning from unorganized data points is a very challenging unsupervised learning and data visualization problem with a great variety of applications. In this paper we present a new algorithm for manifold learning and nonlinear dimension reduction. Based on a set of unorganized data points sampled wi... | [
"['Zhenyue Zhang' 'Hongyuan Zha']"
] |
null | null | 0212011 | null | null | http://arxiv.org/pdf/cs/0212011v1 | 2002-12-08T18:52:33Z | 2002-12-08T18:52:33Z | Mining the Web for Lexical Knowledge to Improve Keyphrase Extraction:
Learning from Labeled and Unlabeled Data | Keyphrases are useful for a variety of purposes, including summarizing, indexing, labeling, categorizing, clustering, highlighting, browsing, and searching. The task of automatic keyphrase extraction is to select keyphrases from within the text of a given document. Automatic keyphrase extraction makes it feasible to ge... | [
"['Peter D. Turney']"
] |
null | null | 0212012 | null | null | http://arxiv.org/pdf/cs/0212012v1 | 2002-12-08T19:06:08Z | 2002-12-08T19:06:08Z | Unsupervised Learning of Semantic Orientation from a
Hundred-Billion-Word Corpus | The evaluative character of a word is called its semantic orientation. A positive semantic orientation implies desirability (e.g., "honest", "intrepid") and a negative semantic orientation implies undesirability (e.g., "disturbing", "superfluous"). This paper introduces a simple algorithm for unsupervised learning of s... | [
"['Peter D. Turney' 'Michael L. Littman']"
] |
null | null | 0212013 | null | null | http://arxiv.org/pdf/cs/0212013v1 | 2002-12-08T19:27:56Z | 2002-12-08T19:27:56Z | Learning to Extract Keyphrases from Text | Many academic journals ask their authors to provide a list of about five to fifteen key words, to appear on the first page of each article. Since these key words are often phrases of two or more words, we prefer to call them keyphrases. There is a surprisingly wide variety of tasks for which keyphrases are useful, as w... | [
"['Peter D. Turney']"
] |
null | null | 0212014 | null | null | http://arxiv.org/pdf/cs/0212014v1 | 2002-12-08T19:40:42Z | 2002-12-08T19:40:42Z | Extraction of Keyphrases from Text: Evaluation of Four Algorithms | This report presents an empirical evaluation of four algorithms for automatically extracting keywords and keyphrases from documents. The four algorithms are compared using five different collections of documents. For each document, we have a target set of keyphrases, which were generated by hand. The target keyphrases ... | [
"['Peter D. Turney']"
] |
null | null | 0212020 | null | null | http://arxiv.org/pdf/cs/0212020v1 | 2002-12-10T15:30:56Z | 2002-12-10T15:30:56Z | Learning Algorithms for Keyphrase Extraction | Many academic journals ask their authors to provide a list of about five to fifteen keywords, to appear on the first page of each article. Since these key words are often phrases of two or more words, we prefer to call them keyphrases. There is a wide variety of tasks for which keyphrases are useful, as we discuss in t... | [
"['Peter D. Turney']"
] |
null | null | 0212023 | null | null | http://arxiv.org/pdf/cs/0212023v1 | 2002-12-10T18:19:54Z | 2002-12-10T18:19:54Z | How to Shift Bias: Lessons from the Baldwin Effect | An inductive learning algorithm takes a set of data as input and generates a hypothesis as output. A set of data is typically consistent with an infinite number of hypotheses; therefore, there must be factors other than the data that determine the output of the learning algorithm. In machine learning, these other facto... | [
"['Peter D. Turney']"
] |
null | null | 0212024 | null | null | http://arxiv.org/pdf/cs/0212024v1 | 2002-12-10T21:59:15Z | 2002-12-10T21:59:15Z | Unsupervised Language Acquisition: Theory and Practice | In this thesis I present various algorithms for the unsupervised machine learning of aspects of natural languages using a variety of statistical models. The scientific object of the work is to examine the validity of the so-called Argument from the Poverty of the Stimulus advanced in favour of the proposition that huma... | [
"['Alexander Clark']"
] |
null | null | 0212028 | null | null | http://arxiv.org/pdf/cs/0212028v1 | 2002-12-11T15:50:41Z | 2002-12-11T15:50:41Z | Technical Note: Bias and the Quantification of Stability | Research on bias in machine learning algorithms has generally been concerned with the impact of bias on predictive accuracy. We believe that there are other factors that should also play a role in the evaluation of bias. One such factor is the stability of the algorithm; in other words, the repeatability of the results... | [
"['Peter D. Turney']"
] |
null | null | 0212029 | null | null | http://arxiv.org/pdf/cs/0212029v1 | 2002-12-11T16:08:36Z | 2002-12-11T16:08:36Z | A Theory of Cross-Validation Error | This paper presents a theory of error in cross-validation testing of algorithms for predicting real-valued attributes. The theory justifies the claim that predicting real-valued attributes requires balancing the conflicting demands of simplicity and accuracy. Furthermore, the theory indicates precisely how these confli... | [
"['Peter D. Turney']"
] |
null | null | 0212030 | null | null | http://arxiv.org/pdf/cs/0212030v1 | 2002-12-11T17:36:00Z | 2002-12-11T17:36:00Z | Theoretical Analyses of Cross-Validation Error and Voting in
Instance-Based Learning | This paper begins with a general theory of error in cross-validation testing of algorithms for supervised learning from examples. It is assumed that the examples are described by attribute-value pairs, where the values are symbolic. Cross-validation requires a set of training examples and a set of testing examples. The... | [
"['Peter D. Turney']"
] |
null | null | 0212031 | null | null | http://arxiv.org/pdf/cs/0212031v1 | 2002-12-11T18:30:59Z | 2002-12-11T18:30:59Z | Contextual Normalization Applied to Aircraft Gas Turbine Engine
Diagnosis | Diagnosing faults in aircraft gas turbine engines is a complex problem. It involves several tasks, including rapid and accurate interpretation of patterns in engine sensor data. We have investigated contextual normalization for the development of a software tool to help engine repair technicians with interpretation of ... | [
"['Peter D. Turney' 'Michael Halasz']"
] |
null | null | 0212032 | null | null | http://arxiv.org/pdf/cs/0212032v1 | 2002-12-11T18:57:42Z | 2002-12-11T18:57:42Z | Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised
Classification of Reviews | This paper presents a simple unsupervised learning algorithm for classifying reviews as recommended (thumbs up) or not recommended (thumbs down). The classification of a review is predicted by the average semantic orientation of the phrases in the review that contain adjectives or adverbs. A phrase has a positive seman... | [
"['Peter D. Turney']"
] |
null | null | 0212033 | null | null | http://arxiv.org/pdf/cs/0212033v1 | 2002-12-11T19:17:06Z | 2002-12-11T19:17:06Z | Mining the Web for Synonyms: PMI-IR versus LSA on TOEFL | This paper presents a simple unsupervised learning algorithm for recognizing synonyms, based on statistical data acquired by querying a Web search engine. The algorithm, called PMI-IR, uses Pointwise Mutual Information (PMI) and Information Retrieval (IR) to measure the similarity of pairs of words. PMI-IR is empirical... | [
"['Peter D. Turney']"
] |
null | null | 0212034 | null | null | http://arxiv.org/pdf/cs/0212034v1 | 2002-12-11T19:42:14Z | 2002-12-11T19:42:14Z | Types of Cost in Inductive Concept Learning | Inductive concept learning is the task of learning to assign cases to a discrete set of classes. In real-world applications of concept learning, there are many different types of cost involved. The majority of the machine learning literature ignores all types of cost (unless accuracy is interpreted as a type of cost me... | [
"['Peter D. Turney']"
] |
null | null | 0212035 | null | null | http://arxiv.org/pdf/cs/0212035v1 | 2002-12-12T19:40:50Z | 2002-12-12T19:40:50Z | Exploiting Context When Learning to Classify | This paper addresses the problem of classifying observations when features are context-sensitive, specifically when the testing set involves a context that is different from the training set. The paper begins with a precise definition of the problem, then general strategies are presented for enhancing the performance o... | [
"['Peter D. Turney']"
] |
null | null | 0212036 | null | null | http://arxiv.org/pdf/cs/0212036v1 | 2002-12-11T21:34:18Z | 2002-12-11T21:34:18Z | Myths and Legends of the Baldwin Effect | This position paper argues that the Baldwin effect is widely misunderstood by the evolutionary computation community. The misunderstandings appear to fall into two general categories. Firstly, it is commonly believed that the Baldwin effect is concerned with the synergy that results when there is an evolving population... | [
"['Peter D. Turney']"
] |
null | null | 0212037 | null | null | http://arxiv.org/pdf/cs/0212037v1 | 2002-12-12T18:14:38Z | 2002-12-12T18:14:38Z | The Management of Context-Sensitive Features: A Review of Strategies | In this paper, we review five heuristic strategies for handling context-sensitive features in supervised machine learning from examples. We discuss two methods for recovering lost (implicit) contextual information. We mention some evidence that hybrid strategies can have a synergetic effect. We then show how the work o... | [
"['Peter D. Turney']"
] |
null | null | 0212038 | null | null | http://arxiv.org/pdf/cs/0212038v1 | 2002-12-12T18:29:02Z | 2002-12-12T18:29:02Z | The Identification of Context-Sensitive Features: A Formal Definition of
Context for Concept Learning | A large body of research in machine learning is concerned with supervised learning from examples. The examples are typically represented as vectors in a multi-dimensional feature space (also known as attribute-value descriptions). A teacher partitions a set of training examples into a finite number of classes. The task... | [
"['Peter D. Turney']"
] |
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