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categories string	doi string	id string	year float64	venue string	link string	updated string	published string	title string	abstract string	authors list
null	null	0212039	null	null	http://arxiv.org/pdf/cs/0212039v1	2002-12-12T18:51:06Z	2002-12-12T18:51:06Z	Low Size-Complexity Inductive Logic Programming: The East-West Challenge Considered as a Problem in Cost-Sensitive Classification	The Inductive Logic Programming community has considered proof-complexity and model-complexity, but, until recently, size-complexity has received little attention. Recently a challenge was issued "to the international computing community" to discover low size-complexity Prolog programs for classifying trains. The chall...	[ "['Peter D. Turney']" ]
null	null	0212040	null	null	http://arxiv.org/pdf/cs/0212040v1	2002-12-12T19:11:11Z	2002-12-12T19:11:11Z	Data Engineering for the Analysis of Semiconductor Manufacturing Data	We have analyzed manufacturing data from several different semiconductor manufacturing plants, using decision tree induction software called Q-YIELD. The software generates rules for predicting when a given product should be rejected. The rules are intended to help the process engineers improve the yield of the product...	[ "['Peter D. Turney']" ]
null	null	0212041	null	null	http://arxiv.org/pdf/cs/0212041v1	2002-12-12T19:26:52Z	2002-12-12T19:26:52Z	Robust Classification with Context-Sensitive Features	This paper addresses the problem of classifying observations when features are context-sensitive, especially 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 of ...	[ "['Peter D. Turney']" ]
null	null	0301007	null	null	http://arxiv.org/pdf/cs/0301007v1	2003-01-09T15:08:47Z	2003-01-09T15:08:47Z	Kalman filter control in the reinforcement learning framework	There is a growing interest in using Kalman-filter models in brain modelling. In turn, it is of considerable importance to make Kalman-filters amenable for reinforcement learning. In the usual formulation of optimal control it is computed off-line by solving a backward recursion. In this technical note we show that sli...	[ "['Istvan Szita' 'Andras Lorincz']" ]
null	null	0301014	null	null	http://arxiv.org/abs/cs/0301014v1	2003-01-16T16:36:15Z	2003-01-16T16:36:15Z	Convergence and Loss Bounds for Bayesian Sequence Prediction	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 generating distribution $mu$ of the sequences $x_1x_2x_3...$ is known. If $mu$ is unknown, but known to belong to a class $M$ one can base ones prediction on the Bayes mix $xi$ defined as...	[ "['Marcus Hutter']" ]
null	null	0302012	null	null	http://arxiv.org/pdf/cs/0302012v2	2003-11-27T10:01:42Z	2003-02-10T14:17:33Z	The New AI: General & Sound & Relevant for Physics	Most traditional artificial intelligence (AI) systems of the past 50 years are either very limited, or based on heuristics, or both. The new millennium, however, has brought substantial progress in the field of theoretically optimal and practically feasible algorithms for prediction, search, inductive inference based o...	[ "['Juergen Schmidhuber']" ]
null	null	0302015	null	null	http://arxiv.org/pdf/cs/0302015v1	2003-02-12T09:39:00Z	2003-02-12T09:39:00Z	Unsupervised Learning in a Framework of Information Compression by Multiple Alignment, Unification and Search	This paper describes a novel approach to unsupervised learning that has been developed within a framework of "information compression by multiple alignment, unification and search" (ICMAUS), designed to integrate learning with other AI functions such as parsing and production of language, fuzzy pattern recognition, pro...	[ "['J. G. Wolff']" ]
null	null	0303025	null	null	http://arxiv.org/pdf/cs/0303025v1	2003-03-24T16:01:46Z	2003-03-24T16:01:46Z	Algorithmic Clustering of Music	We present a fully automatic method for music classification, based only on compression of strings that represent the music pieces. The method uses no background knowledge about music whatsoever: it is completely general and can, without change, be used in different areas like linguistic classification and genomics. It...	[ "['Rudi Cilibrasi' 'Paul Vitanyi' 'Ronald de Wolf']" ]
null	null	0305052	null	null	http://arxiv.org/pdf/cs/0305052v1	2003-05-29T11:11:01Z	2003-05-29T11:11:01Z	On the Existence and Convergence Computable Universal Priors	Solomonoff unified Occam's razor and Epicurus' principle of multiple explanations to one elegant, formal, universal theory of inductive inference, which initiated the field of algorithmic information theory. His central result is that the posterior of his universal semimeasure M converges rapidly to the true sequence g...	[ "['Marcus Hutter']" ]
null	null	0305121	null	null	http://arxiv.org/pdf/math/0305121v1	2003-05-08T17:11:45Z	2003-05-08T17:11:45Z	Robust Estimators under the Imprecise Dirichlet Model	Walley's Imprecise Dirichlet Model (IDM) for categorical data overcomes several fundamental problems which other approaches to uncertainty suffer from. Yet, to be useful in practice, one needs efficient ways for computing the imprecise=robust sets or intervals. The main objective of this work is to derive exact, conser...	[ "['Marcus Hutter']" ]
null	null	0306036	null	null	http://arxiv.org/pdf/cs/0306036v1	2003-06-07T19:21:20Z	2003-06-07T19:21:20Z	Sequence Prediction based on Monotone Complexity	This paper studies sequence prediction based on the monotone Kolmogorov complexity Km=-log m, i.e. based on universal deterministic/one-part MDL. m is extremely close to Solomonoff's prior M, the latter being an excellent predictor in deterministic as well as probabilistic environments, where performance is measured in...	[ "['Marcus Hutter']" ]
null	null	0306055	null	null	http://arxiv.org/abs/nlin/0306055v2	2004-09-14T10:31:58Z	2003-06-26T10:12:58Z	A Model for Prejudiced Learning in Noisy Environments	Based on the heuristics that maintaining presumptions can be beneficial in uncertain environments, we propose a set of basic axioms for learning systems to incorporate the concept of prejudice. The simplest, memoryless model of a deterministic learning rule obeying the axioms is constructed, and shown to be equivalent ...	[ "['Andreas U. Schmidt']" ]
null	null	0306091	null	null	http://arxiv.org/pdf/cs/0306091v2	2004-09-30T13:56:40Z	2003-06-16T13:15:29Z	Universal Sequential Decisions in Unknown Environments	We give a brief introduction to the AIXI model, which unifies and overcomes the limitations of sequential decision theory and universal Solomonoff induction. While the former theory is suited for active agents in known environments, the latter is suited for passive prediction of unknown environments.	[ "['Marcus Hutter']" ]
null	null	0306120	null	null	http://arxiv.org/pdf/cs/0306120v2	2007-03-09T15:14:15Z	2003-06-22T08:00:09Z	Reinforcement Learning with Linear Function Approximation and LQ control Converges	Reinforcement learning is commonly used with function approximation. However, very few positive results are known about the convergence of function approximation based RL control algorithms. In this paper we show that TD(0) and Sarsa(0) with linear function approximation is convergent for a simple class of problems, wh...	[ "['Istvan Szita' 'Andras Lorincz']" ]
null	null	0306126	null	null	http://arxiv.org/pdf/cs/0306126v1	2003-06-24T09:50:29Z	2003-06-24T09:50:29Z	Bayesian Treatment of Incomplete Discrete Data applied to Mutual Information and Feature Selection	Given the joint chances of a pair of random variables one can compute quantities of interest, like the mutual information. The Bayesian treatment of unknown chances involves computing, from a second order prior distribution and the data likelihood, a posterior distribution of the chances. A common treatment of incomple...	[ "['Marcus Hutter' 'Marco Zaffalon']" ]
null	null	0307002	null	null	http://arxiv.org/pdf/cs/0307002v1	2003-07-01T23:22:44Z	2003-07-01T23:22:44Z	AWESOME: A General Multiagent Learning Algorithm that Converges in Self-Play and Learns a Best Response Against Stationary Opponents	A satisfactory multiagent learning algorithm should, {em at a minimum}, learn to play optimally against stationary opponents and converge to a Nash equilibrium in self-play. The algorithm that has come closest, WoLF-IGA, has been proven to have these two properties in 2-player 2-action repeated games--assuming that the...	[ "['Vincent Conitzer' 'Tuomas Sandholm']" ]
null	null	0307006	null	null	http://arxiv.org/pdf/cs/0307006v1	2003-07-03T15:44:36Z	2003-07-03T15:44:36Z	BL-WoLF: A Framework For Loss-Bounded Learnability In Zero-Sum Games	We present BL-WoLF, a framework for learnability in repeated zero-sum games where the cost of learning is measured by the losses the learning agent accrues (rather than the number of rounds). The game is adversarially chosen from some family that the learner knows. The opponent knows the game and the learner's learning...	[ "['Vincent Conitzer' 'Tuomas Sandholm']" ]
null	null	0307038	null	null	http://arxiv.org/pdf/cs/0307038v1	2003-07-16T23:50:53Z	2003-07-16T23:50:53Z	Manifold Learning with Geodesic Minimal Spanning Trees	In the manifold learning problem one seeks to discover a smooth low dimensional surface, i.e., a manifold embedded in a higher dimensional linear vector space, based on a set of measured sample points on the surface. In this paper we consider the closely related problem of estimating the manifold's intrinsic dimension ...	[ "['Jose Costa' 'Alfred Hero']" ]
null	null	0307055	null	null	http://arxiv.org/pdf/cs/0307055v1	2003-07-24T21:09:43Z	2003-07-24T21:09:43Z	Learning Analogies and Semantic Relations	We present an algorithm for learning from unlabeled text, based on the Vector Space Model (VSM) of information retrieval, that can solve verbal analogy questions of the kind found in the Scholastic Aptitude Test (SAT). A verbal analogy has the form A:B::C:D, meaning "A is to B as C is to D"; for example, mason:stone::c...	[ "['Peter D. Turney' 'Michael L. Littman']" ]
null	null	0308025	null	null	http://arxiv.org/pdf/cs/0308025v1	2003-08-16T07:31:57Z	2003-08-16T07:31:57Z	Controlled hierarchical filtering: Model of neocortical sensory processing	A model of sensory information processing is presented. The model assumes that learning of internal (hidden) generative models, which can predict the future and evaluate the precision of that prediction, is of central importance for information extraction. Furthermore, the model makes a bridge to goal-oriented systems ...	[ "['Andras Lorincz']" ]
null	null	0308033	null	null	http://arxiv.org/pdf/cs/0308033v1	2003-08-20T20:42:19Z	2003-08-20T20:42:19Z	Coherent Keyphrase Extraction via Web Mining	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	0309015	null	null	http://arxiv.org/pdf/cs/0309015v1	2003-09-10T13:56:41Z	2003-09-10T13:56:41Z	Reliable and Efficient Inference of Bayesian Networks from Sparse Data by Statistical Learning Theory	To learn (statistical) dependencies among random variables requires exponentially large sample size in the number of observed random variables if any arbitrary joint probability distribution can occur. We consider the case that sparse data strongly suggest that the probabilities can be described by a simple Bayesian ...	[ "['Dominik Janzing' 'Daniel Herrmann']" ]
null	null	0309016	null	null	http://arxiv.org/pdf/cs/0309016v1	2003-09-10T15:11:44Z	2003-09-10T15:11:44Z	Using Simulated Annealing to Calculate the Trembles of Trembling Hand Perfection	Within the literature on non-cooperative game theory, there have been a number of attempts to propose logorithms which will compute Nash equilibria. Rather than derive a new algorithm, this paper shows that the family of algorithms known as Markov chain Monte Carlo (MCMC) can be used to calculate Nash equilibria. MCMC ...	[ "['Stuart McDonald' 'Liam Wagner']" ]
null	null	0309034	null	null	http://arxiv.org/pdf/cs/0309034v1	2003-09-19T16:30:55Z	2003-09-19T16:30:55Z	Measuring Praise and Criticism: Inference of Semantic Orientation from Association	The evaluative character of a word is called its semantic orientation. Positive semantic orientation indicates praise (e.g., "honest", "intrepid") and negative semantic orientation indicates criticism (e.g., "disturbing", "superfluous"). Semantic orientation varies in both direction (positive or negative) and degree (m...	[ "['Peter D. Turney' 'Michael L. Littman']" ]
null	null	0309035	null	null	http://arxiv.org/pdf/cs/0309035v1	2003-09-19T20:13:07Z	2003-09-19T20:13:07Z	Combining Independent Modules to Solve Multiple-choice Synonym and Analogy Problems	Existing statistical approaches to natural language problems are very coarse approximations to the true complexity of language processing. As such, no single technique will be best for all problem instances. Many researchers are examining ensemble methods that combine the output of successful, separately developed modu...	[ "['Peter D. Turney' 'Michael L. Littman' 'Jeffrey Bigham' 'Victor Shnayder']" ]
null	null	0311014	null	null	http://arxiv.org/pdf/cs/0311014v1	2003-11-13T12:02:04Z	2003-11-13T12:02:04Z	Optimality of Universal Bayesian Sequence Prediction for General Loss and Alphabet	Various optimality properties of universal sequence predictors based on Bayes-mixtures in general, and Solomonoff's prediction scheme in particular, will be studied. The probability of observing $x_t$ at time $t$, given past observations $x_1...x_{t-1}$ can be computed with the chain rule if the true generating distrib...	[ "['Marcus Hutter']" ]
null	null	0311042	null	null	http://arxiv.org/pdf/cs/0311042v1	2003-11-27T05:34:04Z	2003-11-27T05:34:04Z	Toward Attribute Efficient Learning Algorithms	We make progress on two important problems regarding attribute efficient learnability. First, we give an algorithm for learning decision lists of length $k$ over $n$ variables using $2^{tilde{O}(k^{1/3})} log n$ examples and time $n^{tilde{O}(k^{1/3})}$. This is the first algorithm for learning decision lists that ha...	[ "['Adam R. Klivans' 'Rocco A. Servedio']" ]
null	null	0312003	null	null	http://arxiv.org/pdf/cs/0312003v1	2003-11-30T00:19:19Z	2003-11-30T00:19:19Z	Hybrid LQG-Neural Controller for Inverted Pendulum System	The paper presents a hybrid system controller, incorporating a neural and an LQG controller. The neural controller has been optimized by genetic algorithms directly on the inverted pendulum system. The failure free optimization process stipulated a relatively small region of the asymptotic stability of the neural contr...	[ "['E. S. Sazonov' 'P. Klinkhachorn' 'R. L. Klein']" ]
null	null	0312004	null	null	http://arxiv.org/pdf/cs/0312004v1	2003-11-30T20:41:18Z	2003-11-30T20:41:18Z	Improving spam filtering by combining Naive Bayes with simple k-nearest neighbor searches	Using naive Bayes for email classification has become very popular within the last few months. They are quite easy to implement and very efficient. In this paper we want to present empirical results of email classification using a combination of naive Bayes and k-nearest neighbor searches. Using this technique we show ...	[ "['Daniel Etzold']" ]
null	null	0312009	null	null	http://arxiv.org/pdf/cs/0312009v1	2003-12-03T22:29:01Z	2003-12-03T22:29:01Z	Failure-Free Genetic Algorithm Optimization of a System Controller Using SAFE/LEARNING Controllers in Tandem	The paper presents a method for failure free genetic algorithm optimization of a system controller. Genetic algorithms present a powerful tool that facilitates producing near-optimal system controllers. Applied to such methods of computational intelligence as neural networks or fuzzy logic, these methods are capable of...	[ "['E. S. Sazonov' 'D. Del Gobbo' 'P. Klinkhachorn' 'R. L. Klein']" ]
null	null	0312018	null	null	http://arxiv.org/abs/cs/0312018v1	2003-12-11T20:07:39Z	2003-12-11T20:07:39Z	Mapping Subsets of Scholarly Information	We illustrate the use of machine learning techniques to analyze, structure, maintain, and evolve a large online corpus of academic literature. An emerging field of research can be identified as part of an existing corpus, permitting the implementation of a more coherent community structure for its practitioners.	[ "['Paul Ginsparg' 'Paul Houle' 'Thorsten Joachims' 'Jae-Hoon Sul']" ]
null	null	0312058	null	null	http://arxiv.org/pdf/cs/0312058v1	2003-12-25T16:45:20Z	2003-12-25T16:45:20Z	Acquiring Lexical Paraphrases from a Single Corpus	This paper studies the potential of identifying lexical paraphrases within a single corpus, focusing on the extraction of verb paraphrases. Most previous approaches detect individual paraphrase instances within a pair (or set) of comparable corpora, each of them containing roughly the same information, and rely on the ...	[ "['Oren Glickman' 'Ido Dagan']" ]
null	null	0312060	null	null	http://arxiv.org/pdf/cs/0312060v1	2003-12-27T21:21:48Z	2003-12-27T21:21:48Z	Part-of-Speech Tagging with Minimal Lexicalization	We use a Dynamic Bayesian Network to represent compactly a variety of sublexical and contextual features relevant to Part-of-Speech (PoS) tagging. The outcome is a flexible tagger (LegoTag) with state-of-the-art performance (3.6% error on a benchmark corpus). We explore the effect of eliminating redundancy and radicall...	[ "['Virginia Savova' 'Leonid Peshkin']" ]
null	null	0401005	null	null	http://arxiv.org/pdf/cs/0401005v1	2004-01-08T07:50:51Z	2004-01-08T07:50:51Z	About Unitary Rating Score Constructing	It is offered to pool test points of different subjects and different aspects of the same subject together in order to get the unitary rating score, by the way of nonlinear transformation of indicator points in accordance with Zipf's distribution. It is proposed to use the well-studied distribution of Intellectuality Q...	[ "['Kromer Victor']" ]
null	null	0401033	null	null	http://arxiv.org/abs/q-bio/0401033v1	2004-01-26T03:50:03Z	2004-01-26T03:50:03Z	Parametric Inference for Biological Sequence Analysis	One of the major successes in computational biology has been the unification, using the graphical model formalism, of a multitude of algorithms for annotating and comparing biological sequences. Graphical models that have been applied towards these problems include hidden Markov models for annotation, tree models for p...	[ "['Lior Pachter' 'Bernd Sturmfels']" ]
null	null	0402021	null	null	http://arxiv.org/pdf/cs/0402021v1	2004-02-11T15:45:14Z	2004-02-11T15:45:14Z	A Numerical Example on the Principles of Stochastic Discrimination	Studies on ensemble methods for classification suffer from the difficulty of modeling the complementary strengths of the components. Kleinberg's theory of stochastic discrimination (SD) addresses this rigorously via mathematical notions of enrichment, uniformity, and projectability of an ensemble. We explain these conc...	[ "['Tin Kam Ho']" ]
null	null	0402029	null	null	http://arxiv.org/pdf/q-bio/0402029v2	2004-10-26T16:21:17Z	2004-02-12T22:36:01Z	Fluctuation-dissipation theorem and models of learning	Advances in statistical learning theory have resulted in a multitude of different designs of learning machines. But which ones are implemented by brains and other biological information processors? We analyze how various abstract Bayesian learners perform on different data and argue that it is difficult to determine wh...	[ "['Ilya Nemenman']" ]
null	null	0402032	null	null	http://arxiv.org/pdf/cs/0402032v1	2004-02-15T07:40:45Z	2004-02-15T07:40:45Z	Fitness inheritance in the Bayesian optimization algorithm	This paper describes how fitness inheritance can be used to estimate fitness for a proportion of newly sampled candidate solutions in the Bayesian optimization algorithm (BOA). The goal of estimating fitness for some candidate solutions is to reduce the number of fitness evaluations for problems where fitness evaluatio...	[ "['Martin Pelikan' 'Kumara Sastry']" ]
null	null	0403025	null	null	http://arxiv.org/pdf/cs/0403025v1	2004-03-15T16:33:55Z	2004-03-15T16:33:55Z	Distribution of Mutual Information from Complete and Incomplete Data	Mutual information is widely used, in a descriptive way, to measure the stochastic dependence of categorical random variables. In order to address questions such as the reliability of the descriptive value, one must consider sample-to-population inferential approaches. This paper deals with the posterior distribution o...	[ "['Marcus Hutter' 'Marco Zaffalon']" ]
null	null	0403031	null	null	http://arxiv.org/pdf/cs/0403031v2	2004-03-20T07:51:11Z	2004-03-19T17:13:55Z	Concept of E-machine: How does a "dynamical" brain learn to process "symbolic" information? Part I	The human brain has many remarkable information processing characteristics that deeply puzzle scientists and engineers. Among the most important and the most intriguing of these characteristics are the brain's broad universality as a learning system and its mysterious ability to dynamically change (reconfigure) its beh...	[ "['Victor Eliashberg']" ]
null	null	0403038	null	null	http://arxiv.org/pdf/cs/0403038v1	2004-03-23T15:17:53Z	2004-03-23T15:17:53Z	Tournament versus Fitness Uniform Selection	In evolutionary algorithms a critical parameter that must be tuned is that of selection pressure. If it is set too low then the rate of convergence towards the optimum is likely to be slow. Alternatively if the selection pressure is set too high the system is likely to become stuck in a local optimum due to a loss of d...	[ "['Shane Legg' 'Marcus Hutter' 'Akshat Kumar']" ]
null	null	0404032	null	null	http://arxiv.org/pdf/cs/0404032v1	2004-04-15T02:59:10Z	2004-04-15T02:59:10Z	When Do Differences Matter? On-Line Feature Extraction Through Cognitive Economy	For an intelligent agent to be truly autonomous, it must be able to adapt its representation to the requirements of its task as it interacts with the world. Most current approaches to on-line feature extraction are ad hoc; in contrast, this paper presents an algorithm that bases judgments of state compatibility and sta...	[ "['David J. Finton']" ]
null	null	0404057	null	null	http://arxiv.org/pdf/cs/0404057v1	2004-04-28T15:58:35Z	2004-04-28T15:58:35Z	Convergence of Discrete MDL for Sequential Prediction	We study the properties of the Minimum Description Length principle for sequence prediction, considering a two-part MDL estimator which is chosen from a countable class of models. This applies in particular to the important case of universal sequence prediction, where the model class corresponds to all algorithms for s...	[ "['Jan Poland' 'Marcus Hutter']" ]
null	null	0405043	null	null	http://arxiv.org/pdf/cs/0405043v2	2004-05-12T20:37:07Z	2004-05-12T16:41:01Z	Prediction with Expert Advice by Following the Perturbed Leader for General Weights	When applying aggregating strategies to Prediction with Expert Advice, the learning rate must be adaptively tuned. The natural choice of sqrt(complexity/current loss) renders the analysis of Weighted Majority derivatives quite complicated. In particular, for arbitrary weights there have been no results proven so far. T...	[ "['Marcus Hutter' 'Jan Poland']" ]
null	null	0405104	null	null	http://arxiv.org/pdf/cs/0405104v1	2004-05-27T11:26:18Z	2004-05-27T11:26:18Z	Knowledge Reduction and Discovery based on Demarcation Information	Knowledge reduction, includes attribute reduction and value reduction, is an important topic in rough set literature. It is also closely relevant to other fields, such as machine learning and data mining. In this paper, an algorithm called TWI-SQUEEZE is proposed. It can find a reduct, or an irreducible attribute subse...	[ "['Yuguo He']" ]
null	null	0406011	null	null	http://arxiv.org/pdf/cs/0406011v1	2004-06-06T18:57:05Z	2004-06-06T18:57:05Z	Blind Construction of Optimal Nonlinear Recursive Predictors for Discrete Sequences	We present a new method for nonlinear prediction of discrete random sequences under minimal structural assumptions. We give a mathematical construction for optimal predictors of such processes, in the form of hidden Markov models. We then describe an algorithm, CSSR (Causal-State Splitting Reconstruction), which approx...	[ "['Cosma Rohilla Shalizi' 'Kristina Lisa Shalizi']" ]
null	null	0406077	null	null	http://arxiv.org/pdf/math/0406077v1	2004-06-04T09:11:18Z	2004-06-04T09:11:18Z	A tutorial introduction to the minimum description length principle	This tutorial provides an overview of and introduction to Rissanen's Minimum Description Length (MDL) Principle. The first chapter provides a conceptual, entirely non-technical introduction to the subject. It serves as a basis for the technical introduction given in the second chapter, in which all the ideas of the fir...	[ "['Peter Grunwald']" ]
null	null	0406221	null	null	http://arxiv.org/pdf/math/0406221v1	2004-06-10T16:36:54Z	2004-06-10T16:36:54Z	Suboptimal behaviour of Bayes and MDL in classification under misspecification	We show that forms of Bayesian and MDL inference that are often applied to classification problems can be inconsistent. This means there exists a learning problem such that for all amounts of data the generalization errors of the MDL classifier and the Bayes classifier relative to the Bayesian posterior both remain b...	[ "['Peter Grunwald' 'John Langford']" ]
null	null	0407016	null	null	http://arxiv.org/pdf/cs/0407016v1	2004-07-06T22:18:25Z	2004-07-06T22:18:25Z	Learning for Adaptive Real-time Search	Real-time heuristic search is a popular model of acting and learning in intelligent autonomous agents. Learning real-time search agents improve their performance over time by acquiring and refining a value function guiding the application of their actions. As computing the perfect value function is typically intractabl...	[ "['Vadim Bulitko']" ]
null	null	0407039	null	null	http://arxiv.org/pdf/cs/0407039v1	2004-07-16T10:36:49Z	2004-07-16T10:36:49Z	On the Convergence Speed of MDL Predictions for Bernoulli Sequences	We consider the Minimum Description Length principle for online sequence prediction. If the underlying model class is discrete, then the total expected square loss is a particularly interesting performance measure: (a) this quantity is bounded, implying convergence with probability one, and (b) it additionally specifie...	[ "['Jan Poland' 'Marcus Hutter']" ]
null	null	0407057	null	null	http://arxiv.org/pdf/cs/0407057v1	2004-07-23T12:43:28Z	2004-07-23T12:43:28Z	Universal Convergence of Semimeasures on Individual Random Sequences	Solomonoff's central result on induction is that the posterior of a universal semimeasure M converges rapidly and with probability 1 to the true sequence generating posterior mu, if the latter is computable. Hence, M is eligible as a universal sequence predictor in case of unknown mu. Despite some nearby results and pr...	[ "['Marcus Hutter' 'Andrej Muchnik']" ]
null	null	0407065	null	null	http://arxiv.org/pdf/cs/0407065v1	2004-07-29T19:46:01Z	2004-07-29T19:46:01Z	Word Sense Disambiguation by Web Mining for Word Co-occurrence Probabilities	This paper describes the National Research Council (NRC) Word Sense Disambiguation (WSD) system, as applied to the English Lexical Sample (ELS) task in Senseval-3. The NRC system approaches WSD as a classical supervised machine learning problem, using familiar tools such as the Weka machine learning software and Brill'...	[ "['Peter D. Turney']" ]
null	null	0408001	null	null	http://arxiv.org/pdf/cs/0408001v1	2004-07-31T14:04:04Z	2004-07-31T14:04:04Z	Semantic Linking - a Context-Based Approach to Interactivity in Hypermedia	The semantic Web initiates new, high level access schemes to online content and applications. One area of superior need for a redefined content exploration is given by on-line educational applications and their concepts of interactivity in the framework of open hypermedia systems. In the present paper we discuss aspect...	[ "['Michael Engelhardt' 'Thomas C. Schmidt']" ]
null	null	0408004	null	null	http://arxiv.org/pdf/cs/0408004v1	2004-07-31T22:16:37Z	2004-07-31T22:16:37Z	Hypermedia Learning Objects System - On the Way to a Semantic Educational Web	While eLearning systems become more and more popular in daily education, available applications lack opportunities to structure, annotate and manage their contents in a high-level fashion. General efforts to improve these deficits are taken by initiatives to define rich meta data sets and a semanticWeb layer. In the pr...	[ "['Michael Engelhardt' 'Andreas Kárpáti' 'Torsten Rack' 'Ivette Schmidt'\n 'Thomas C. Schmidt']" ]
null	null	0408007	null	null	http://arxiv.org/pdf/cs/0408007v1	2004-08-02T21:24:41Z	2004-08-02T21:24:41Z	Online convex optimization in the bandit setting: gradient descent without a gradient	We consider a the general online convex optimization framework introduced by Zinkevich. In this setting, there is a sequence of convex functions. Each period, we must choose a signle point (from some feasible set) and pay a cost equal to the value of the next function on our chosen point. Zinkevich shows that, if the e...	[ "['Abraham D. Flaxman' 'Adam Tauman Kalai' 'H. Brendan McMahan']" ]
null	null	0408039	null	null	http://arxiv.org/abs/nlin/0408039v2	2004-11-22T23:33:13Z	2004-08-20T05:17:14Z	Stability and Diversity in Collective Adaptation	We derive a class of macroscopic differential equations that describe collective adaptation, starting from a discrete-time stochastic microscopic model. The behavior of each agent is a dynamic balance between adaptation that locally achieves the best action and memory loss that leads to randomized behavior. We show tha...	[ "['Yuzuru Sato' 'Eizo Akiyama' 'James P. Crutchfield']" ]
null	null	0408048	null	null	http://arxiv.org/pdf/cs/0408048v1	2004-08-21T16:57:34Z	2004-08-21T16:57:34Z	Journal of New Democratic Methods: An Introduction	This paper describes a new breed of academic journals that use statistical machine learning techniques to make them more democratic. In particular, not only can anyone submit an article, but anyone can also become a reviewer. Machine learning is used to decide which reviewers accurately represent the views of the journ...	[ "['John David Funge']" ]
null	null	0408058	null	null	http://arxiv.org/pdf/cs/0408058v1	2004-08-25T20:25:43Z	2004-08-25T20:25:43Z	Non-negative matrix factorization with sparseness constraints	Non-negative matrix factorization (NMF) is a recently developed technique for finding parts-based, linear representations of non-negative data. Although it has successfully been applied in several applications, it does not always result in parts-based representations. In this paper, we show how explicitly incorporating...	[ "['Patrik O. Hoyer']" ]
null	null	0408146	null	null	http://arxiv.org/pdf/math/0408146v1	2004-08-11T06:38:50Z	2004-08-11T06:38:50Z	Learning a Machine for the Decision in a Partially Observable Markov Universe	In this paper, we are interested in optimal decisions in a partially observable Markov universe. Our viewpoint departs from the dynamic programming viewpoint: we are directly approximating an optimal strategic tree depending on the observation. This approximation is made by means of a parameterized probabilistic law. I...	[ "['Frederic Dambreville']" ]
null	null	0410004	null	null	http://arxiv.org/pdf/cs/0410004v1	2004-10-02T07:19:49Z	2004-10-02T07:19:49Z	Applying Policy Iteration for Training Recurrent Neural Networks	Recurrent neural networks are often used for learning time-series data. Based on a few assumptions we model this learning task as a minimization problem of a nonlinear least-squares cost function. The special structure of the cost function allows us to build a connection to reinforcement learning. We exploit this conne...	[ "['I. Szita' 'A. Lorincz']" ]
null	null	0410015	null	null	http://arxiv.org/pdf/cs/0410015v1	2004-10-07T10:57:08Z	2004-10-07T10:57:08Z	L1 regularization is better than L2 for learning and predicting chaotic systems	Emergent behaviors are in the focus of recent research interest. It is then of considerable importance to investigate what optimizations suit the learning and prediction of chaotic systems, the putative candidates for emergence. We have compared L1 and L2 regularizations on predicting chaotic time series using linear r...	[ "['Z. Szabo' 'A. Lorincz']" ]
null	null	0410017	null	null	http://arxiv.org/pdf/cs/0410017v1	2004-10-07T17:20:56Z	2004-10-07T17:20:56Z	Automated Pattern Detection--An Algorithm for Constructing Optimally Synchronizing Multi-Regular Language Filters	In the computational-mechanics structural analysis of one-dimensional cellular automata the following automata-theoretic analogue of the emph{change-point problem} from time series analysis arises: emph{Given a string $sigma$ and a collection ${mc{D}_i}$ of finite automata, identify the regions of $sigma$ that belong t...	[ "['Carl S. McTague' 'James P. Crutchfield']" ]
null	null	0410036	null	null	http://arxiv.org/pdf/cs/0410036v2	2005-09-09T17:48:53Z	2004-10-15T20:25:24Z	Self-Organised Factorial Encoding of a Toroidal Manifold	It is shown analytically how a neural network can be used optimally to encode input data that is derived from a toroidal manifold. The case of a 2-layer network is considered, where the output is assumed to be a set of discrete neural firing events. The network objective function measures the average Euclidean error th...	[ "['Stephen Luttrell']" ]
null	null	0410042	null	null	http://arxiv.org/pdf/cs/0410042v1	2004-10-18T10:50:28Z	2004-10-18T10:50:28Z	Neural Architectures for Robot Intelligence	We argue that the direct experimental approaches to elucidate the architecture of higher brains may benefit from insights gained from exploring the possibilities and limits of artificial control architectures for robot systems. We present some of our recent work that has been motivated by that view and that is centered...	[ "['H. Ritter' 'J. J. Steil' 'C. Noelker' 'F. Roethling' 'P. C. McGuire']" ]
null	null	0411099	null	null	http://arxiv.org/pdf/cs/0411099v1	2004-11-30T08:36:59Z	2004-11-30T08:36:59Z	A Note on the PAC Bayesian Theorem	We prove general exponential moment inequalities for averages of [0,1]-valued iid random variables and use them to tighten the PAC Bayesian Theorem. The logarithmic dependence on the sample count in the enumerator of the PAC Bayesian bound is halved.	[ "['Andreas Maurer']" ]
null	null	0411140	null	null	http://arxiv.org/abs/quant-ph/0411140v2	2005-07-29T21:05:02Z	2004-11-18T20:14:16Z	Improved Bounds on Quantum Learning Algorithms	In this article we give several new results on the complexity of algorithms that learn Boolean functions from quantum queries and quantum examples. Hunziker et al. conjectured that for any class C of Boolean functions, the number of quantum black-box queries which are required to exactly identify an unknown function ...	[ "['Alp Atici' 'Rocco A. Servedio']" ]
null	null	0411515	null	null	http://arxiv.org/pdf/math/0411515v1	2004-11-23T16:39:07Z	2004-11-23T16:39:07Z	Fast Non-Parametric Bayesian Inference on Infinite Trees	Given i.i.d. data from an unknown distribution, we consider the problem of predicting future items. An adaptive way to estimate the probability density is to recursively subdivide the domain to an appropriate data-dependent granularity. A Bayesian would assign a data-independent prior probability to "subdivide", which ...	[ "['Marcus Hutter']" ]
null	null	0412003	null	null	http://arxiv.org/pdf/cs/0412003v1	2004-12-01T16:32:49Z	2004-12-01T16:32:49Z	Mining Heterogeneous Multivariate Time-Series for Learning Meaningful Patterns: Application to Home Health Telecare	For the last years, time-series mining has become a challenging issue for researchers. An important application lies in most monitoring purposes, which require analyzing large sets of time-series for learning usual patterns. Any deviation from this learned profile is then considered as an unexpected situation. Moreover...	[ "['Florence Duchene' 'Catherine Garbay' 'Vincent Rialle']" ]
null	null	0412024	null	null	http://arxiv.org/pdf/cs/0412024v1	2004-12-06T21:50:18Z	2004-12-06T21:50:18Z	Human-Level Performance on Word Analogy Questions by Latent Relational Analysis	This paper introduces Latent Relational Analysis (LRA), a method for measuring relational similarity. LRA has potential applications in many areas, including information extraction, word sense disambiguation, machine translation, and information retrieval. Relational similarity is correspondence between relations, in c...	[ "['Peter D. Turney']" ]
null	null	0412098	null	null	http://arxiv.org/pdf/cs/0412098v3	2007-05-30T17:23:04Z	2004-12-21T16:05:36Z	The Google Similarity Distance	Words and phrases acquire meaning from the way they are used in society, from their relative semantics to other words and phrases. For computers the equivalent of `society' is `database,' and the equivalent of `use' is `way to search the database.' We present a new theory of similarity between words and phrases based o...	[ "['Rudi Cilibrasi' 'Paul M. B. Vitanyi']" ]
null	null	0412106	null	null	http://arxiv.org/pdf/cs/0412106v1	2004-12-23T15:21:40Z	2004-12-23T15:21:40Z	Online Learning of Aggregate Knowledge about Non-linear Preferences Applied to Negotiating Prices and Bundles	In this paper, we consider a form of multi-issue negotiation where a shop negotiates both the contents and the price of bundles of goods with his customers. We present some key insights about, as well as a procedure for, locating mutually beneficial alternatives to the bundle currently under negotiation. The essence of...	[ "['Koye Somefun' 'Tomas Klos' 'Han La Poutré']" ]
null	null	0501018	null	null	http://arxiv.org/pdf/cs/0501018v1	2005-01-10T21:03:14Z	2005-01-10T21:03:14Z	Combining Independent Modules in Lexical Multiple-Choice Problems	Existing statistical approaches to natural language problems are very coarse approximations to the true complexity of language processing. As such, no single technique will be best for all problem instances. Many researchers are examining ensemble methods that combine the output of multiple modules to create more accur...	[ "['Peter D. Turney' 'Michael L. Littman' 'Jeffrey Bigham' 'Victor Shnayder']" ]
null	null	0501028	null	null	http://arxiv.org/pdf/cs/0501028v1	2005-01-14T15:50:28Z	2005-01-14T15:50:28Z	An Empirical Study of MDL Model Selection with Infinite Parametric Complexity	Parametric complexity is a central concept in MDL model selection. In practice it often turns out to be infinite, even for quite simple models such as the Poisson and Geometric families. In such cases, MDL model selection as based on NML and Bayesian inference based on Jeffreys' prior can not be used. Several ways to r...	[ "['Steven de Rooij' 'Peter Grunwald']" ]
null	null	0501063	null	null	http://arxiv.org/abs/cs/0501063v1	2005-01-22T22:07:18Z	2005-01-22T22:07:18Z	Bandit Problems with Side Observations	An extension of the traditional two-armed bandit problem is considered, in which the decision maker has access to some side information before deciding which arm to pull. At each time t, before making a selection, the decision maker is able to observe a random variable X_t that provides some information on the rewards ...	[ "['Chih-Chun Wang' 'Sanjeev R. Kulkarni' 'H. Vincent Poor']" ]
null	null	0502004	null	null	http://arxiv.org/pdf/cs/0502004v1	2005-02-01T13:42:49Z	2005-02-01T13:42:49Z	Asymptotic Log-loss of Prequential Maximum Likelihood Codes	We analyze the Dawid-Rissanen prequential maximum likelihood codes relative to one-parameter exponential family models M. If data are i.i.d. according to an (essentially) arbitrary P, then the redundancy grows at rate c/2 ln n. We show that c=v1/v2, where v1 is the variance of P, and v2 is the variance of the distribut...	[ "['Peter Grunwald' 'Steven de Rooij']" ]
null	null	0502016	null	null	http://arxiv.org/pdf/cs/0502016v1	2005-02-03T19:54:02Z	2005-02-03T19:54:02Z	Stability Analysis for Regularized Least Squares Regression	We discuss stability for a class of learning algorithms with respect to noisy labels. The algorithms we consider are for regression, and they involve the minimization of regularized risk functionals, such as L(f) := 1/N sum_i (f(x_i)-y_i)^2+ lambda \|\|f\|\|_H^2. We shall call the algorithm `stable' if, when y_i is a noisy...	[ "['Cynthia Rudin']" ]
null	null	0502017	null	null	http://arxiv.org/pdf/cs/0502017v1	2005-02-03T21:11:54Z	2005-02-03T21:11:54Z	Estimating mutual information and multi--information in large networks	We address the practical problems of estimating the information relations that characterize large networks. Building on methods developed for analysis of the neural code, we show that reliable estimates of mutual information can be obtained with manageable computational effort. The same methods allow estimation of high...	[ "['Noam Slonim' 'Gurinder S. Atwal' 'Gasper Tkacik' 'William Bialek']" ]
null	null	0502067	null	null	http://arxiv.org/pdf/cs/0502067v1	2005-02-15T14:59:49Z	2005-02-15T14:59:49Z	Master Algorithms for Active Experts Problems based on Increasing Loss Values	We specify an experts algorithm with the following characteristics: (a) it uses only feedback from the actions actually chosen (bandit setup), (b) it can be applied with countably infinite expert classes, and (c) it copes with losses that may grow in time appropriately slowly. We prove loss bounds against an adaptive a...	[ "['Jan Poland' 'Marcus Hutter']" ]
null	null	0502074	null	null	http://arxiv.org/abs/cs/0502074v2	2005-10-17T07:59:18Z	2005-02-17T14:58:28Z	On sample complexity for computational pattern recognition	In statistical setting of the pattern recognition problem the number of examples required to approximate an unknown labelling function is linear in the VC dimension of the target learning class. In this work we consider the question whether such bounds exist if we restrict our attention to computable pattern recognitio...	[ "['Daniil Ryabko']" ]
null	null	0502076	null	null	http://arxiv.org/abs/cs/0502076v2	2006-07-05T05:29:36Z	2005-02-18T01:31:53Z	Learning nonsingular phylogenies and hidden Markov models	In this paper we study the problem of learning phylogenies and hidden Markov models. We call a Markov model nonsingular if all transition matrices have determinants bounded away from 0 (and 1). We highlight the role of the nonsingularity condition for the learning problem. Learning hidden Markov models without the nons...	[ "['Elchanan Mossel' 'Sébastien Roch']" ]
null	null	0502086	null	null	http://arxiv.org/pdf/cs/0502086v1	2005-02-22T09:51:16Z	2005-02-22T09:51:16Z	The Self-Organization of Speech Sounds	The speech code is a vehicle of language: it defines a set of forms used by a community to carry information. Such a code is necessary to support the linguistic interactions that allow humans to communicate. How then may a speech code be formed prior to the existence of linguistic interactions? Moreover, the human spee...	[ "['Pierre-Yves Oudeyer']" ]
null	null	0502315	null	null	http://arxiv.org/pdf/math/0502315v1	2005-02-15T16:26:36Z	2005-02-15T16:26:36Z	Strong Asymptotic Assertions for Discrete MDL in Regression and Classification	We study the properties of the MDL (or maximum penalized complexity) estimator for Regression and Classification, where the underlying model class is countable. We show in particular a finite bound on the Hellinger losses under the only assumption that there is a "true" model contained in the class. This implies almost...	[ "['Jan Poland' 'Marcus Hutter']" ]
null	null	0503026	null	null	http://arxiv.org/pdf/cs/0503026v1	2005-03-11T12:38:30Z	2005-03-11T12:38:30Z	On Generalized Computable Universal Priors and their Convergence	Solomonoff unified Occam's razor and Epicurus' principle of multiple explanations to one elegant, formal, universal theory of inductive inference, which initiated the field of algorithmic information theory. His central result is that the posterior of the universal semimeasure M converges rapidly to the true sequence g...	[ "['Marcus Hutter']" ]
null	null	0503071	null	null	http://arxiv.org/abs/cs/0503071v2	2005-09-30T02:05:50Z	2005-03-26T05:13:51Z	Consistency in Models for Distributed Learning under Communication Constraints	Motivated by sensor networks and other distributed settings, several models for distributed learning are presented. The models differ from classical works in statistical pattern recognition by allocating observations of an independent and identically distributed (i.i.d.) sampling process amongst members of a network of...	[ "['Joel B. Predd' 'Sanjeev R. Kulkarni' 'H. Vincent Poor']" ]
null	null	0503072	null	null	http://arxiv.org/abs/cs/0503072v1	2005-03-26T05:42:06Z	2005-03-26T05:42:06Z	Distributed Learning in Wireless Sensor Networks	The problem of distributed or decentralized detection and estimation in applications such as wireless sensor networks has often been considered in the framework of parametric models, in which strong assumptions are made about a statistical description of nature. In certain applications, such assumptions are warranted a...	[ "['Joel B. Predd' 'Sanjeev R. Kulkarni' 'H. Vincent Poor']" ]
null	null	0504001	null	null	http://arxiv.org/pdf/cs/0504001v1	2005-03-31T23:04:28Z	2005-03-31T23:04:28Z	Probabilistic and Team PFIN-type Learning: General Properties	We consider the probability hierarchy for Popperian FINite learning and study the general properties of this hierarchy. We prove that the probability hierarchy is decidable, i.e. there exists an algorithm that receives p_1 and p_2 and answers whether PFIN-type learning with the probability of success p_1 is equivalent ...	[ "['Andris Ambainis']" ]
null	null	0504042	null	null	http://arxiv.org/pdf/cs/0504042v1	2005-04-11T17:45:09Z	2005-04-11T17:45:09Z	The Bayesian Decision Tree Technique with a Sweeping Strategy	The uncertainty of classification outcomes is of crucial importance for many safety critical applications including, for example, medical diagnostics. In such applications the uncertainty of classification can be reliably estimated within a Bayesian model averaging technique that allows the use of prior information. De...	[ "['V. Schetinin' 'J. E. Fieldsend' 'D. Partridge' 'W. J. Krzanowski'\n 'R. M. Everson' 'T. C. Bailey' 'A. Hernandez']" ]
null	null	0504043	null	null	http://arxiv.org/pdf/cs/0504043v1	2005-04-11T17:53:35Z	2005-04-11T17:53:35Z	Experimental Comparison of Classification Uncertainty for Randomised and Bayesian Decision Tree Ensembles	In this paper we experimentally compare the classification uncertainty of the randomised Decision Tree (DT) ensemble technique and the Bayesian DT technique with a restarting strategy on a synthetic dataset as well as on some datasets commonly used in the machine learning community. For quantitative evaluation of class...	[ "['V. Schetinin' 'D. Partridge' 'W. J. Krzanowski' 'R. M. Everson'\n 'J. E. Fieldsend' 'T. C. Bailey' 'A. Hernandez']" ]
null	null	0504052	null	null	http://arxiv.org/pdf/cs/0504052v1	2005-04-13T13:22:49Z	2005-04-13T13:22:49Z	Learning Multi-Class Neural-Network Models from Electroencephalograms	We describe a new algorithm for learning multi-class neural-network models from large-scale clinical electroencephalograms (EEGs). This algorithm trains hidden neurons separately to classify all the pairs of classes. To find best pairwise classifiers, our algorithm searches for input variables which are relevant to the...	[ "['Vitaly Schetinin' 'Joachim Schult' 'Burkhart Scheidt' 'Valery Kuriakin']" ]
null	null	0504054	null	null	http://arxiv.org/pdf/cs/0504054v1	2005-04-13T13:40:38Z	2005-04-13T13:40:38Z	Learning from Web: Review of Approaches	Knowledge discovery is defined as non-trivial extraction of implicit, previously unknown and potentially useful information from given data. Knowledge extraction from web documents deals with unstructured, free-format documents whose number is enormous and rapidly growing. The artificial neural networks are well suitab...	[ "['Vitaly Schetinin']" ]
null	null	0504063	null	null	http://arxiv.org/pdf/cs/0504063v1	2005-04-14T07:57:01Z	2005-04-14T07:57:01Z	Selection in Scale-Free Small World	In this paper we compare the performance characteristics of our selection based learning algorithm for Web crawlers with the characteristics of the reinforcement learning algorithm. The task of the crawlers is to find new information on the Web. The selection algorithm, called weblog update, modifies the starting URL l...	[ "['Zs. Palotai' 'Cs. Farkas' 'A. Lorincz']" ]
null	null	0504069	null	null	http://arxiv.org/pdf/cs/0504069v1	2005-04-14T10:47:38Z	2005-04-14T10:47:38Z	A Neural-Network Technique to Learn Concepts from Electroencephalograms	A new technique is presented developed to learn multi-class concepts from clinical electroencephalograms. A desired concept is represented as a neuronal computational model consisting of the input, hidden, and output neurons. In this model the hidden neurons learn independently to classify the electroencephalogram segm...	[ "['Vitaly Schetinin' 'Joachim Schult']" ]
null	null	0504070	null	null	http://arxiv.org/pdf/cs/0504070v1	2005-04-14T10:49:55Z	2005-04-14T10:49:55Z	The Combined Technique for Detection of Artifacts in Clinical Electroencephalograms of Sleeping Newborns	In this paper we describe a new method combining the polynomial neural network and decision tree techniques in order to derive comprehensible classification rules from clinical electroencephalograms (EEGs) recorded from sleeping newborns. These EEGs are heavily corrupted by cardiac, eye movement, muscle and noise artif...	[ "['Vitaly Schetinin' 'Joachim Schult']" ]
null	null	0504078	null	null	http://arxiv.org/pdf/cs/0504078v1	2005-04-16T16:48:49Z	2005-04-16T16:48:49Z	Adaptive Online Prediction by Following the Perturbed Leader	When applying aggregating strategies to Prediction with Expert Advice, the learning rate must be adaptively tuned. The natural choice of sqrt(complexity/current loss) renders the analysis of Weighted Majority derivatives quite complicated. In particular, for arbitrary weights there have been no results proven so far. T...	[ "['Marcus Hutter' 'Jan Poland']" ]
null	null	0504086	null	null	http://arxiv.org/pdf/cs/0504086v1	2005-04-19T15:01:25Z	2005-04-19T15:01:25Z	Componentwise Least Squares Support Vector Machines	This chapter describes componentwise Least Squares Support Vector Machines (LS-SVMs) for the estimation of additive models consisting of a sum of nonlinear components. The primal-dual derivations characterizing LS-SVMs for the estimation of the additive model result in a single set of linear equations with size growing...	[ "['Kristiaan Pelckmans' 'Ivan Goethals' 'Jos De Brabanter'\n 'Johan A. K. Suykens' 'Bart De Moor']" ]
null	null	0505028	null	null	http://arxiv.org/pdf/cs/0505028v3	2005-08-16T12:43:07Z	2005-05-11T16:45:58Z	A linear memory algorithm for Baum-Welch training	Background: Baum-Welch training is an expectation-maximisation algorithm for training the emission and transition probabilities of hidden Markov models in a fully automated way. Methods and results: We introduce a linear space algorithm for Baum-Welch training. For a hidden Markov model with M states, T free transiti...	[ "['Istvan Miklos' 'Irmtraud M. Meyer']" ]
null	null	0505064	null	null	http://arxiv.org/abs/cs/0505064v1	2005-05-24T14:53:49Z	2005-05-24T14:53:49Z	Multi-Modal Human-Machine Communication for Instructing Robot Grasping Tasks	A major challenge for the realization of intelligent robots is to supply them with cognitive abilities in order to allow ordinary users to program them easily and intuitively. One way of such programming is teaching work tasks by interactive demonstration. To make this effective and convenient for the user, the machine...	[ "['P. C. McGuire' 'J. Fritsch' 'J. J. Steil' 'F. Roethling' 'G. A. Fink'\n 'S. Wachsmuth' 'G. Sagerer' 'H. Ritter']" ]
null	null	0505083	null	null	http://arxiv.org/pdf/cs/0505083v1	2005-05-30T21:12:00Z	2005-05-30T21:12:00Z	Defensive forecasting	We consider how to make probability forecasts of binary labels. Our main mathematical result is that for any continuous gambling strategy used for detecting disagreement between the forecasts and the actual labels, there exists a forecasting strategy whose forecasts are ideal as far as this gambling strategy is concern...	[ "['Vladimir Vovk' 'Akimichi Takemura' 'Glenn Shafer']" ]
null	null	0506004	null	null	http://arxiv.org/pdf/cs/0506004v4	2006-07-01T13:46:30Z	2005-06-01T14:03:20Z	Non-asymptotic calibration and resolution	We analyze a new algorithm for probability forecasting of binary observations on the basis of the available data, without making any assumptions about the way the observations are generated. The algorithm is shown to be well calibrated and to have good resolution for long enough sequences of observations and for a suit...	[ "['Vladimir Vovk']" ]
null	null	0506007	null	null	http://arxiv.org/pdf/cs/0506007v2	2005-09-24T16:55:14Z	2005-06-02T13:26:43Z	Defensive forecasting for linear protocols	We consider a general class of forecasting protocols, called "linear protocols", and discuss several important special cases, including multi-class forecasting. Forecasting is formalized as a game between three players: Reality, whose role is to generate observations; Forecaster, whose goal is to predict the observatio...	[ "['Vladimir Vovk' 'Ilia Nouretdinov' 'Akimichi Takemura' 'Glenn Shafer']" ]

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