categories string | doi string | id string | year float64 | venue string | link string | updated string | published string | title string | abstract string | authors list |
|---|---|---|---|---|---|---|---|---|---|---|
null | null | 0506022 | null | null | http://arxiv.org/abs/cs/0506022v1 | 2005-06-08T09:07:23Z | 2005-06-08T09:07:23Z | Asymptotics of Discrete MDL for Online Prediction | Minimum Description Length (MDL) is an important principle for induction and prediction, with strong relations to optimal Bayesian learning. This paper deals with learning non-i.i.d. processes by means of two-part MDL, where the underlying model class is countable. We consider the online learning framework, i.e. observ... | [
"['Jan Poland' 'Marcus Hutter']"
] |
null | null | 0506041 | null | null | http://arxiv.org/pdf/cs/0506041v3 | 2005-09-02T14:27:18Z | 2005-06-11T18:11:22Z | Competitive on-line learning with a convex loss function | We consider the problem of sequential decision making under uncertainty in which the loss caused by a decision depends on the following binary observation. In competitive on-line learning, the goal is to design decision algorithms that are almost as good as the best decision rules in a wide benchmark class, without mak... | [
"['Vladimir Vovk']"
] |
null | null | 0506057 | null | null | http://arxiv.org/pdf/cs/0506057v2 | 2005-07-21T02:43:12Z | 2005-06-14T04:00:38Z | About one 3-parameter Model of Testing | This article offers a 3-parameter model of testing, with 1) the difference between the ability level of the examinee and item difficulty; 2) the examinee discrimination and 3) the item discrimination as model parameters. | [
"['Kromer Victor']"
] |
null | null | 0506075 | null | null | http://arxiv.org/pdf/cs/0506075v1 | 2005-06-17T20:10:43Z | 2005-06-17T20:10:43Z | Seeing stars: Exploiting class relationships for sentiment
categorization with respect to rating scales | We address the rating-inference problem, wherein rather than simply decide whether a review is "thumbs up" or "thumbs down", as in previous sentiment analysis work, one must determine an author's evaluation with respect to a multi-point scale (e.g., one to five "stars"). This task represents an interesting twist on sta... | [
"['Bo Pang' 'Lillian Lee']"
] |
null | null | 0506085 | null | null | http://arxiv.org/pdf/cs/0506085v1 | 2005-06-22T21:21:13Z | 2005-06-22T21:21:13Z | On the Job Training | We propose a new framework for building and evaluating machine learning algorithms. We argue that many real-world problems require an agent which must quickly learn to respond to demands, yet can continue to perform and respond to new training throughout its useful life. We give a framework for how such agents can be b... | [
"['Jason E. Holt']"
] |
null | null | 0506095 | null | null | http://arxiv.org/pdf/cs/0506095v1 | 2005-06-27T04:07:34Z | 2005-06-27T04:07:34Z | Deriving a Stationary Dynamic Bayesian Network from a Logic Program with
Recursive Loops | Recursive loops in a logic program present a challenging problem to the PLP framework. On the one hand, they loop forever so that the PLP backward-chaining inferences would never stop. On the other hand, they generate cyclic influences, which are disallowed in Bayesian networks. Therefore, in existing PLP approaches lo... | [
"['Y. D. Shen' 'Q. Yang' 'J. H. You' 'L. Y. Yuan']"
] |
null | null | 0506101 | null | null | http://arxiv.org/pdf/cs/0506101v1 | 2005-06-29T20:26:33Z | 2005-06-29T20:26:33Z | Efficient Multiclass Implementations of L1-Regularized Maximum Entropy | This paper discusses the application of L1-regularized maximum entropy modeling or SL1-Max [9] to multiclass categorization problems. A new modification to the SL1-Max fast sequential learning algorithm is proposed to handle conditional distributions. Furthermore, unlike most previous studies, the present research goes... | [
"['Patrick Haffner' 'Steven Phillips' 'Rob Schapire']"
] |
null | null | 0507033 | null | null | http://arxiv.org/pdf/cs/0507033v2 | 2005-11-14T08:18:49Z | 2005-07-13T05:45:28Z | Multiresolution Kernels | We present in this work a new methodology to design kernels on data which is structured with smaller components, such as text, images or sequences. This methodology is a template procedure which can be applied on most kernels on measures and takes advantage of a more detailed "bag of components" representation of the o... | [
"['Marco Cuturi' 'Kenji Fukumizu']"
] |
null | null | 0507039 | null | null | http://arxiv.org/abs/cs/0507039v1 | 2005-07-18T00:45:12Z | 2005-07-18T00:45:12Z | Distributed Regression in Sensor Networks: Training Distributively with
Alternating Projections | Wireless sensor networks (WSNs) have attracted considerable attention in recent years and motivate a host of new challenges for distributed signal processing. The problem of distributed or decentralized estimation has often been considered in the context of parametric models. However, the success of parametric methods ... | [
"['Joel B. Predd' 'Sanjeev R. Kulkarni' 'H. Vincent Poor']"
] |
null | null | 0507040 | null | null | http://arxiv.org/pdf/cs/0507040v1 | 2005-07-18T08:10:10Z | 2005-07-18T08:10:10Z | Pattern Recognition for Conditionally Independent Data | In this work we consider the task of relaxing the i.i.d assumption in pattern recognition (or classification), aiming to make existing learning algorithms applicable to a wider range of tasks. Pattern recognition is guessing a discrete label of some object based on a set of given examples (pairs of objects and labels).... | [
"['Daniil Ryabko']"
] |
null | null | 0507041 | null | null | http://arxiv.org/pdf/cs/0507041v1 | 2005-07-18T12:34:53Z | 2005-07-18T12:34:53Z | Monotone Conditional Complexity Bounds on Future Prediction Errors | We bound the future loss when predicting any (computably) stochastic sequence online. Solomonoff finitely bounded the total deviation of his universal predictor M from the true distribution m by the algorithmic complexity of m. Here we assume we are at a time t>1 and already observed x=x_1...x_t. We bound the future pr... | [
"['Alexey Chernov' 'Marcus Hutter']"
] |
null | null | 0507044 | null | null | http://arxiv.org/pdf/cs/0507044v1 | 2005-07-18T14:33:56Z | 2005-07-18T14:33:56Z | Defensive Universal Learning with Experts | This paper shows how universal learning can be achieved with expert advice. To this aim, 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 loss... | [
"['Jan Poland' 'Marcus Hutter']"
] |
null | null | 0507062 | null | null | http://arxiv.org/pdf/cs/0507062v1 | 2005-07-26T05:00:27Z | 2005-07-26T05:00:27Z | FPL Analysis for Adaptive Bandits | A main problem of "Follow the Perturbed Leader" strategies for online decision problems is that regret bounds are typically proven against oblivious adversary. In partial observation cases, it was not clear how to obtain performance guarantees against adaptive adversary, without worsening the bounds. We propose a conce... | [
"['Jan Poland']"
] |
null | null | 0508007 | null | null | http://arxiv.org/pdf/cs/0508007v4 | 2010-12-27T08:29:34Z | 2005-08-01T18:55:57Z | Regularity of Position Sequences | A person is given a numbered sequence of positions on a sheet of paper. The person is asked, "Which will be the next (or the next after that) position?" Everyone has an opinion as to how he or she would proceed. There are regular sequences for which there is general agreement on how to continue. However, there are less... | [
"['Manfred Harringer']"
] |
null | null | 0508027 | null | null | http://arxiv.org/abs/cs/0508027v1 | 2005-08-03T16:09:00Z | 2005-08-03T16:09:00Z | Expectation maximization as message passing | Based on prior work by Eckford, it is shown how expectation maximization (EM) may be viewed, and used, as a message passing algorithm in factor graphs. | [
"['J. Dauwels' 'S. Korl' 'H. -A. Loeliger']"
] |
null | null | 0508043 | null | null | http://arxiv.org/pdf/cs/0508043v1 | 2005-08-05T10:16:16Z | 2005-08-05T10:16:16Z | Sequential Predictions based on Algorithmic 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 universal prior M, the latter being an excellent predictor in deterministic as well as probabilistic environments, where performance is m... | [
"['Marcus Hutter']"
] |
null | null | 0508053 | null | null | http://arxiv.org/pdf/cs/0508053v1 | 2005-08-10T19:35:57Z | 2005-08-10T19:35:57Z | Measuring Semantic Similarity by Latent Relational Analysis | This paper introduces Latent Relational Analysis (LRA), a method for measuring semantic similarity. LRA measures similarity in the semantic relations between two pairs of words. When two pairs have a high degree of relational similarity, they are analogous. For example, the pair cat:meow is analogous to the pair dog:ba... | [
"['Peter D. Turney']"
] |
null | null | 0508073 | null | null | http://arxiv.org/pdf/cs/0508073v1 | 2005-08-16T16:27:25Z | 2005-08-16T16:27:25Z | Universal Learning of Repeated Matrix Games | We study and compare the learning dynamics of two universal learning algorithms, one based on Bayesian learning and the other on prediction with expert advice. Both approaches have strong asymptotic performance guarantees. When confronted with the task of finding good long-term strategies in repeated 2x2 matrix games, ... | [
"['Jan Poland' 'Marcus Hutter']"
] |
null | null | 0508103 | null | null | http://arxiv.org/pdf/cs/0508103v1 | 2005-08-23T20:21:56Z | 2005-08-23T20:21:56Z | Corpus-based Learning of 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 SAT college entrance exam. A verbal analogy has the form A:B::C:D, meaning "A is to B as C is to D"; for example, mason:stone::carpen... | [
"['Peter D. Turney' 'Michael L. Littman']"
] |
null | null | 0508319 | null | null | http://arxiv.org/pdf/math/0508319v1 | 2005-08-17T10:13:04Z | 2005-08-17T10:13:04Z | Combinations and Mixtures of Optimal Policies in Unichain Markov
Decision Processes are Optimal | We show that combinations of optimal (stationary) policies in unichain Markov decision processes are optimal. That is, let M be a unichain Markov decision process with state space S, action space A and policies pi_j^*: S -> A (1leq jleq n) with optimal average infinite horizon reward. Then any combination pi of these p... | [
"['Ronald Ortner']"
] |
null | null | 0509055 | null | null | http://arxiv.org/pdf/cs/0509055v1 | 2005-09-19T04:57:26Z | 2005-09-19T04:57:26Z | Learning Optimal Augmented Bayes Networks | Naive Bayes is a simple Bayesian classifier with strong independence assumptions among the attributes. This classifier, desipte its strong independence assumptions, often performs well in practice. It is believed that relaxing the independence assumptions of a naive Bayes classifier may improve the classification accur... | [
"['Vikas Hamine' 'Paul Helman']"
] |
null | null | 0510038 | null | null | http://arxiv.org/abs/cs/0510038v4 | 2007-06-26T14:00:17Z | 2005-10-14T19:26:34Z | Learning Unions of $ω(1)$-Dimensional Rectangles | We consider the problem of learning unions of rectangles over the domain $[b]^n$, in the uniform distribution membership query learning setting, where both b and n are "large". We obtain poly$(n, log b)$-time algorithms for the following classes: - poly$(n log b)$-way Majority of $O(frac{log(n log b)} {log log(n log ... | [
"['Alp Atici' 'Rocco A. Servedio']"
] |
null | null | 0510080 | null | null | http://arxiv.org/pdf/cs/0510080v1 | 2005-10-25T22:14:33Z | 2005-10-25T22:14:33Z | When Ignorance is Bliss | It is commonly-accepted wisdom that more information is better, and that information should never be ignored. Here we argue, using both a Bayesian and a non-Bayesian analysis, that in some situations you are better off ignoring information if your uncertainty is represented by a set of probability measures. These inclu... | [
"['Peter D. Grunwald' 'Joseph Y. Halpern']"
] |
null | null | 0511011 | null | null | http://arxiv.org/pdf/cs/0511011v1 | 2005-11-02T23:44:34Z | 2005-11-02T23:44:34Z | The Impact of Social Networks on Multi-Agent Recommender Systems | Awerbuch et al.'s approach to distributed recommender systems (DRSs) is to have agents sample products at random while randomly querying one another for the best item they have found; we improve upon this by adding a communication network. Agents can only communicate with their immediate neighbors in the network, but n... | [
"['Hamilton Link' 'Jared Saia' 'Terran Lane' 'Randall A. LaViolette']"
] |
null | null | 0511015 | null | null | http://arxiv.org/pdf/nlin/0511015v1 | 2005-11-09T14:41:00Z | 2005-11-09T14:41:00Z | Combinatorial Approach to Object Analysis | We present a perceptional mathematical model for image and signal analysis. A resemblance measure is defined, and submitted to an innovating combinatorial optimization algorithm. Numerical Simulations are also presented | [
"['Rami Kanhouche']"
] |
null | null | 0511058 | null | null | http://arxiv.org/pdf/cs/0511058v2 | 2006-01-24T23:27:14Z | 2005-11-15T17:13:50Z | On-line regression competitive with reproducing kernel Hilbert spaces | We consider the problem of on-line prediction of real-valued labels, assumed bounded in absolute value by a known constant, of new objects from known labeled objects. The prediction algorithm's performance is measured by the squared deviation of the predictions from the actual labels. No stochastic assumptions are made... | [
"['Vladimir Vovk']"
] |
null | null | 0511075 | null | null | http://arxiv.org/pdf/cs/0511075v1 | 2005-11-21T01:47:53Z | 2005-11-21T01:47:53Z | Identifying Interaction Sites in "Recalcitrant" Proteins: Predicted
Protein and Rna Binding Sites in Rev Proteins of Hiv-1 and Eiav Agree with
Experimental Data | Protein-protein and protein nucleic acid interactions are vitally important for a wide range of biological processes, including regulation of gene expression, protein synthesis, and replication and assembly of many viruses. We have developed machine learning approaches for predicting which amino acids of a protein part... | [
"['Michael Terribilini' 'Jae-Hyung Lee' 'Changhui Yan' 'Robert L. Jernigan'\n 'Susan Carpenter' 'Vasant Honavar' 'Drena Dobbs']"
] |
null | null | 0511087 | null | null | http://arxiv.org/abs/cs/0511087v1 | 2005-11-25T10:59:35Z | 2005-11-25T10:59:35Z | Robust Inference of Trees | This paper is concerned with the reliable inference of optimal tree-approximations to the dependency structure of an unknown distribution generating data. The traditional approach to the problem measures the dependency strength between random variables by the index called mutual information. In this paper reliability i... | [
"['Marco Zaffalon' 'Marcus Hutter']"
] |
null | null | 0511088 | null | null | http://arxiv.org/pdf/cs/0511088v1 | 2005-11-25T15:57:56Z | 2005-11-25T15:57:56Z | Bounds on Query Convergence | The problem of finding an optimum using noisy evaluations of a smooth cost function arises in many contexts, including economics, business, medicine, experiment design, and foraging theory. We derive an asymptotic bound E[ (x_t - x*)^2 ] >= O(1/sqrt(t)) on the rate of convergence of a sequence (x_0, x_1, >...) generate... | [
"['Barak A. Pearlmutter']"
] |
null | null | 0511105 | null | null | http://arxiv.org/pdf/cs/0511105v1 | 2005-11-30T14:15:17Z | 2005-11-30T14:15:17Z | The Signed Distance Function: A New Tool for Binary Classification | From a geometric perspective most nonlinear binary classification algorithms, including state of the art versions of Support Vector Machine (SVM) and Radial Basis Function Network (RBFN) classifiers, and are based on the idea of reconstructing indicator functions. We propose instead to use reconstruction of the signed ... | [
"['Erik M. Boczko' 'Todd R. Young']"
] |
null | null | 0511108 | null | null | http://arxiv.org/pdf/cs/0511108v1 | 2005-11-30T20:23:19Z | 2005-11-30T20:23:19Z | Parameter Estimation of Hidden Diffusion Processes: Particle Filter vs.
Modified Baum-Welch Algorithm | We propose a new method for the estimation of parameters of hidden diffusion processes. Based on parametrization of the transition matrix, the Baum-Welch algorithm is improved. The algorithm is compared to the particle filter in application to the noisy periodic systems. It is shown that the modified Baum-Welch algorit... | [
"['A. Benabdallah' 'G. Radons']"
] |
null | null | 0511159 | null | null | http://arxiv.org/abs/cond-mat/0511159v2 | 2005-12-09T14:17:08Z | 2005-11-07T13:48:01Z | Learning by message-passing in networks of discrete synapses | We show that a message-passing process allows to store in binary "material" synapses a number of random patterns which almost saturates the information theoretic bounds. We apply the learning algorithm to networks characterized by a wide range of different connection topologies and of size comparable with that of biolo... | [
"['Alfredo Braunstein' 'Riccardo Zecchina']"
] |
null | null | 0512015 | null | null | http://arxiv.org/pdf/cs/0512015v3 | 2007-05-17T18:57:12Z | 2005-12-03T19:21:33Z | Joint fixed-rate universal lossy coding and identification of
continuous-alphabet memoryless sources | The problem of joint universal source coding and identification is considered in the setting of fixed-rate lossy coding of continuous-alphabet memoryless sources. For a wide class of bounded distortion measures, it is shown that any compactly parametrized family of $R^d$-valued i.i.d. sources with absolutely continuous... | [
"['Maxim Raginsky']"
] |
null | null | 0512018 | null | null | http://arxiv.org/pdf/cs/0512018v2 | 2006-03-21T12:31:02Z | 2005-12-05T06:57:39Z | DAMNED: A Distributed and Multithreaded Neural Event-Driven simulation
framework | In a Spiking Neural Networks (SNN), spike emissions are sparsely and irregularly distributed both in time and in the network architecture. Since a current feature of SNNs is a low average activity, efficient implementations of SNNs are usually based on an Event-Driven Simulation (EDS). On the other hand, simulations of... | [
"['Anthony Mouraud' 'Didier Puzenat' 'Hélène Paugam-Moisy']"
] |
null | null | 0512050 | null | null | http://arxiv.org/pdf/cs/0512050v1 | 2005-12-13T13:25:57Z | 2005-12-13T13:25:57Z | Preference Learning in Terminology Extraction: A ROC-based approach | A key data preparation step in Text Mining, Term Extraction selects the terms, or collocation of words, attached to specific concepts. In this paper, the task of extracting relevant collocations is achieved through a supervised learning algorithm, exploiting a few collocations manually labelled as relevant/irrelevant. ... | [
"['Jérôme Azé' 'Mathieu Roche' 'Yves Kodratoff' 'Michèle Sebag']"
] |
null | null | 0512053 | null | null | http://arxiv.org/pdf/cs/0512053v1 | 2005-12-13T22:01:09Z | 2005-12-13T22:01:09Z | Online Learning and Resource-Bounded Dimension: Winnow Yields New Lower
Bounds for Hard Sets | We establish a relationship between the online mistake-bound model of learning and resource-bounded dimension. This connection is combined with the Winnow algorithm to obtain new results about the density of hard sets under adaptive reductions. This improves previous work of Fu (1995) and Lutz and Zhao (2000), and solv... | [
"['John M. Hitchcock']"
] |
null | null | 0512059 | null | null | http://arxiv.org/pdf/cs/0512059v2 | 2006-01-25T17:36:52Z | 2005-12-14T20:03:30Z | Competing with wild prediction rules | We consider the problem of on-line prediction competitive with a benchmark class of continuous but highly irregular prediction rules. It is known that if the benchmark class is a reproducing kernel Hilbert space, there exists a prediction algorithm whose average loss over the first N examples does not exceed the averag... | [
"['Vladimir Vovk']"
] |
null | null | 0512063 | null | null | http://arxiv.org/abs/cs/0512063v1 | 2005-12-15T14:51:36Z | 2005-12-15T14:51:36Z | Complex Random Vectors and ICA Models: Identifiability, Uniqueness and
Separability | In this paper the conditions for identifiability, separability and uniqueness of linear complex valued independent component analysis (ICA) models are established. These results extend the well-known conditions for solving real-valued ICA problems to complex-valued models. Relevant properties of complex random vectors ... | [
"['Jan Eriksson' 'Visa Koivunen']"
] |
null | null | 0601044 | null | null | http://arxiv.org/pdf/cs/0601044v1 | 2006-01-11T15:39:16Z | 2006-01-11T15:39:16Z | Genetic Programming, Validation Sets, and Parsimony Pressure | Fitness functions based on test cases are very common in Genetic Programming (GP). This process can be assimilated to a learning task, with the inference of models from a limited number of samples. This paper is an investigation on two methods to improve generalization in GP-based learning: 1) the selection of the best... | [
"['Christian Gagné' 'Marc Schoenauer' 'Marc Parizeau' 'Marco Tomassini']"
] |
null | null | 0601074 | null | null | http://arxiv.org/abs/cs/0601074v2 | 2006-05-11T21:07:30Z | 2006-01-17T00:08:05Z | Joint universal lossy coding and identification of i.i.d. vector sources | The problem of joint universal source coding and modeling, addressed by Rissanen in the context of lossless codes, is generalized to fixed-rate lossy coding of continuous-alphabet memoryless sources. We show that, for bounded distortion measures, any compactly parametrized family of i.i.d. real vector sources with abso... | [
"['Maxim Raginsky']"
] |
null | null | 0601087 | null | null | http://arxiv.org/pdf/cs/0601087v1 | 2006-01-20T05:40:44Z | 2006-01-20T05:40:44Z | Processing of Test Matrices with Guessing Correction | It is suggested to insert into test matrix 1s for correct responses, 0s for response refusals, and negative corrective elements for incorrect responses. With the classical test theory approach test scores of examinees and items are calculated traditionally as sums of matrix elements, organized in rows and columns. Corr... | [
"['Kromer Victor']"
] |
null | null | 0601089 | null | null | http://arxiv.org/abs/cs/0601089v1 | 2006-01-20T17:46:45Z | 2006-01-20T17:46:45Z | Distributed Kernel Regression: An Algorithm for Training Collaboratively | This paper addresses the problem of distributed learning under communication constraints, motivated by distributed signal processing in wireless sensor networks and data mining with distributed databases. After formalizing a general model for distributed learning, an algorithm for collaboratively training regularized k... | [
"['Joel B. Predd' 'Sanjeev R. Kulkarni' 'H. Vincent Poor']"
] |
null | null | 0601115 | null | null | http://arxiv.org/pdf/cs/0601115v2 | 2006-02-24T17:29:14Z | 2006-01-27T16:52:09Z | Decision Making with Side Information and Unbounded Loss Functions | We consider the problem of decision-making with side information and unbounded loss functions. Inspired by probably approximately correct learning model, we use a slightly different model that incorporates the notion of side information in a more generic form to make it applicable to a broader class of applications inc... | [
"['Majid Fozunbal' 'Ton Kalker']"
] |
null | null | 0602053 | null | null | http://arxiv.org/pdf/cs/0602053v1 | 2006-02-14T23:57:01Z | 2006-02-14T23:57:01Z | How to Beat the Adaptive Multi-Armed Bandit | The multi-armed bandit is a concise model for the problem of iterated decision-making under uncertainty. In each round, a gambler must pull one of $K$ arms of a slot machine, without any foreknowledge of their payouts, except that they are uniformly bounded. A standard objective is to minimize the gambler's regret, def... | [
"['Varsha Dani' 'Thomas P. Hayes']"
] |
null | null | 0602062 | null | null | http://arxiv.org/pdf/cs/0602062v1 | 2006-02-17T08:57:44Z | 2006-02-17T08:57:44Z | Learning rational stochastic languages | Given a finite set of words w1,...,wn independently drawn according to a fixed unknown distribution law P called a stochastic language, an usual goal in Grammatical Inference is to infer an estimate of P in some class of probabilistic models, such as Probabilistic Automata (PA). Here, we study the class of rational sto... | [
"['François Denis' 'Yann Esposito' 'Amaury Habrard']"
] |
null | null | 0602092 | null | null | http://arxiv.org/pdf/cs/0602092v1 | 2006-02-27T05:22:15Z | 2006-02-27T05:22:15Z | Inconsistent parameter estimation in Markov random fields: Benefits in
the computation-limited setting | Consider the problem of joint parameter estimation and prediction in a Markov random field: i.e., the model parameters are estimated on the basis of an initial set of data, and then the fitted model is used to perform prediction (e.g., smoothing, denoising, interpolation) on a new noisy observation. Working under the r... | [
"['Martin J. Wainwright']"
] |
null | null | 0602093 | null | null | http://arxiv.org/pdf/cs/0602093v1 | 2006-02-27T10:08:26Z | 2006-02-27T10:08:26Z | Rational stochastic languages | The goal of the present paper is to provide a systematic and comprehensive study of rational stochastic languages over a semiring K in {Q, Q +, R, R+}. A rational stochastic language is a probability distribution over a free monoid Sigma^* which is rational over K, that is which can be generated by a multiplicity autom... | [
"['François Denis' 'Yann Esposito']"
] |
null | null | 0602183 | null | null | http://arxiv.org/abs/cond-mat/0602183v1 | 2006-02-07T18:29:35Z | 2006-02-07T18:29:35Z | Nonlinear parametric model for Granger causality of time series | We generalize a previously proposed approach for nonlinear Granger causality of time series, based on radial basis function. The proposed model is not constrained to be additive in variables from the two time series and can approximate any function of these variables, still being suitable to evaluate causality. Usefuln... | [
"['Daniele Marinazzo' 'Mario Pellicoro' 'Sebastiano Stramaglia']"
] |
null | null | 0602505 | null | null | http://arxiv.org/abs/math/0602505v1 | 2006-02-22T16:29:05Z | 2006-02-22T16:29:05Z | MDL Convergence Speed for Bernoulli Sequences | The Minimum Description Length principle for online sequence estimation/prediction in a proper learning setup is studied. If the underlying model class is discrete, then the total expected square loss is a particularly interesting performance measure: (a) this quantity is finitely bounded, implying convergence with pro... | [
"['Jan Poland' 'Marcus Hutter']"
] |
null | null | 0603023 | null | null | http://arxiv.org/pdf/cs/0603023v1 | 2006-03-07T08:44:29Z | 2006-03-07T08:44:29Z | Metric State Space Reinforcement Learning for a Vision-Capable Mobile
Robot | We address the problem of autonomously learning controllers for vision-capable mobile robots. We extend McCallum's (1995) Nearest-Sequence Memory algorithm to allow for general metrics over state-action trajectories. We demonstrate the feasibility of our approach by successfully running our algorithm on a real mobile r... | [
"['Viktor Zhumatiy' 'Faustino Gomez' 'Marcus Hutter' 'Juergen Schmidhuber']"
] |
null | null | 0603090 | null | null | http://arxiv.org/abs/cs/0603090v2 | 2006-07-28T13:41:39Z | 2006-03-22T22:52:23Z | Topological Grammars for Data Approximation | A method of {it topological grammars} is proposed for multidimensional data approximation. For data with complex topology we define a {it principal cubic complex} of low dimension and given complexity that gives the best approximation for the dataset. This complex is a generalization of linear and non-linear principal ... | [
"['A. N. Gorban' 'N. R. Sumner' 'A. Y. Zinovyev']"
] |
null | null | 0603110 | null | null | http://arxiv.org/pdf/cs/0603110v1 | 2006-03-28T16:22:42Z | 2006-03-28T16:22:42Z | Asymptotic Learnability of Reinforcement Problems with Arbitrary
Dependence | We address the problem of reinforcement learning in which observations may exhibit an arbitrary form of stochastic dependence on past observations and actions. The task for an agent is to attain the best possible asymptotic reward where the true generating environment is unknown but belongs to a known countable family ... | [
"['Daniil Ryabko' 'Marcus Hutter']"
] |
null | null | 0604010 | null | null | http://arxiv.org/pdf/cs/0604010v2 | 2018-06-04T18:17:32Z | 2006-04-05T10:29:48Z | Nearly optimal exploration-exploitation decision thresholds | While in general trading off exploration and exploitation in reinforcement learning is hard, under some formulations relatively simple solutions exist. In this paper, we first derive upper bounds for the utility of selecting different actions in the multi-armed bandit setting. Unlike the common statistical upper confid... | [
"['Christos Dimitrakakis']"
] |
null | null | 0604011 | null | null | http://arxiv.org/pdf/cs/0604011v2 | 2006-04-06T12:23:30Z | 2006-04-05T18:07:31Z | Semi-Supervised Learning -- A Statistical Physics Approach | We present a novel approach to semi-supervised learning which is based on statistical physics. Most of the former work in the field of semi-supervised learning classifies the points by minimizing a certain energy function, which corresponds to a minimal k-way cut solution. In contrast to these methods, we estimate the ... | [
"['Gad Getz' 'Noam Shental' 'Eytan Domany']"
] |
null | null | 0604015 | null | null | http://arxiv.org/pdf/cs/0604015v1 | 2006-04-06T00:08:24Z | 2006-04-06T00:08:24Z | Revealing the Autonomous System Taxonomy: The Machine Learning Approach | Although the Internet AS-level topology has been extensively studied over the past few years, little is known about the details of the AS taxonomy. An AS "node" can represent a wide variety of organizations, e.g., large ISP, or small private business, university, with vastly different network characteristics, external ... | [
"['Xenofontas Dimitropoulos' 'Dmitri Krioukov' 'George Riley' 'kc claffy']"
] |
null | null | 0604046 | null | null | http://arxiv.org/pdf/cs/0604046v1 | 2006-04-11T14:00:22Z | 2006-04-11T14:00:22Z | Concerning the differentiability of the energy function in vector
quantization algorithms | The adaptation rule for Vector Quantization algorithms, and consequently the convergence of the generated sequence, depends on the existence and properties of a function called the energy function, defined on a topological manifold. Our aim is to investigate the conditions of existence of such a function for a class of... | [
"['Dominique Lepetz' 'Max Nemoz-Gaillard' 'Michael Aupetit']"
] |
null | null | 0604102 | null | null | http://arxiv.org/pdf/cs/0604102v1 | 2006-04-25T19:32:03Z | 2006-04-25T19:32:03Z | HCI and Educational Metrics as Tools for VLE Evaluation | The general set of HCI and Educational principles are considered and a classification system constructed. A frequency analysis of principles is used to obtain the most significant set. Metrics are devised to provide objective measures of these principles and a consistent testing regime devised. These principles are use... | [
"['Vita Hinze-Hoare']"
] |
null | null | 0604233 | null | null | http://arxiv.org/pdf/math/0604233v1 | 2006-04-11T05:41:15Z | 2006-04-11T05:41:15Z | Generalization error bounds in semi-supervised classification under the
cluster assumption | We consider semi-supervised classification when part of the available data is unlabeled. These unlabeled data can be useful for the classification problem when we make an assumption relating the behavior of the regression function to that of the marginal distribution. Seeger (2000) proposed the well-known "cluster assu... | [
"['Philippe Rigollet']"
] |
null | null | 0605009 | null | null | http://arxiv.org/pdf/cs/0605009v1 | 2006-05-03T07:47:21Z | 2006-05-03T07:47:21Z | On the Foundations of Universal Sequence Prediction | Solomonoff completed the Bayesian framework by providing a rigorous, unique, formal, and universal choice for the model class and the prior. We discuss in breadth how and in which sense universal (non-i.i.d.) sequence prediction solves various (philosophical) problems of traditional Bayesian sequence prediction. We sho... | [
"['Marcus Hutter']"
] |
null | null | 0605024 | null | null | http://arxiv.org/pdf/cs/0605024v1 | 2006-05-06T16:56:43Z | 2006-05-06T16:56:43Z | A Formal Measure of Machine Intelligence | A fundamental problem in artificial intelligence is that nobody really knows what intelligence is. The problem is especially acute when we need to consider artificial systems which are significantly different to humans. In this paper we approach this problem in the following way: We take a number of well known informal... | [
"['Shane Legg' 'Marcus Hutter']"
] |
null | null | 0605035 | null | null | http://arxiv.org/pdf/cs/0605035v1 | 2006-05-08T22:05:24Z | 2006-05-08T22:05:24Z | Query Chains: Learning to Rank from Implicit Feedback | This paper presents a novel approach for using clickthrough data to learn ranked retrieval functions for web search results. We observe that users searching the web often perform a sequence, or chain, of queries with a similar information need. Using query chains, we generate new types of preference judgments from sear... | [
"['Filip Radlinski' 'Thorsten Joachims']"
] |
null | null | 0605036 | null | null | http://arxiv.org/pdf/cs/0605036v1 | 2006-05-08T23:38:13Z | 2006-05-08T23:38:13Z | Evaluating the Robustness of Learning from Implicit Feedback | This paper evaluates the robustness of learning from implicit feedback in web search. In particular, we create a model of user behavior by drawing upon user studies in laboratory and real-world settings. The model is used to understand the effect of user behavior on the performance of a learning algorithm for ranked re... | [
"['Filip Radlinski' 'Thorsten Joachims']"
] |
null | null | 0605037 | null | null | http://arxiv.org/pdf/cs/0605037v1 | 2006-05-09T01:53:22Z | 2006-05-09T01:53:22Z | Minimally Invasive Randomization for Collecting Unbiased Preferences
from Clickthrough Logs | Clickthrough data is a particularly inexpensive and plentiful resource to obtain implicit relevance feedback for improving and personalizing search engines. However, it is well known that the probability of a user clicking on a result is strongly biased toward documents presented higher in the result set irrespective o... | [
"['Filip Radlinski' 'Thorsten Joachims']"
] |
null | null | 0605040 | null | null | http://arxiv.org/pdf/cs/0605040v1 | 2006-05-09T10:39:03Z | 2006-05-09T10:39:03Z | General Discounting versus Average Reward | Consider an agent interacting with an environment in cycles. In every interaction cycle the agent is rewarded for its performance. We compare the average reward U from cycle 1 to m (average value) with the future discounted reward V from cycle k to infinity (discounted value). We consider essentially arbitrary (non-geo... | [
"['Marcus Hutter']"
] |
null | null | 0605042 | null | null | http://arxiv.org/abs/astro-ph/0605042v1 | 2006-05-01T20:42:03Z | 2006-05-01T20:42:03Z | How accurate are the time delay estimates in gravitational lensing? | We present a novel approach to estimate the time delay between light curves of multiple images in a gravitationally lensed system, based on Kernel methods in the context of machine learning. We perform various experiments with artificially generated irregularly-sampled data sets to study the effect of the various level... | [
"['Juan C. Cuevas-Tello' 'Peter Tino' 'Somak Raychaudhury']"
] |
null | null | 0605048 | null | null | http://arxiv.org/pdf/cs/0605048v1 | 2006-05-11T03:27:12Z | 2006-05-11T03:27:12Z | On Learning Thresholds of Parities and Unions of Rectangles in Random
Walk Models | In a recent breakthrough, [Bshouty et al., 2005] obtained the first passive-learning algorithm for DNFs under the uniform distribution. They showed that DNFs are learnable in the Random Walk and Noise Sensitivity models. We extend their results in several directions. We first show that thresholds of parities, a natural... | [
"['S. Roch']"
] |
null | null | 0605498 | null | null | http://arxiv.org/pdf/math/0605498v1 | 2006-05-18T07:47:58Z | 2006-05-18T07:47:58Z | Cross-Entropic Learning of a Machine for the Decision in a Partially
Observable Universe | Revision of the paper previously entitled "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 universe. Our approach is to directly approximate an optimal strategic tree depending on the observation. This approxi... | [
"['Frederic Dambreville']"
] |
null | null | 0606077 | null | null | http://arxiv.org/pdf/cs/0606077v1 | 2006-06-16T16:33:23Z | 2006-06-16T16:33:23Z | On Sequence Prediction for Arbitrary Measures | Suppose we are given two probability measures on the set of one-way infinite finite-alphabet sequences and consider the question when one of the measures predicts the other, that is, when conditional probabilities converge (in a certain sense) when one of the measures is chosen to generate the sequence. This question m... | [
"['Daniil Ryabko' 'Marcus Hutter']"
] |
null | null | 0606093 | null | null | http://arxiv.org/pdf/cs/0606093v1 | 2006-06-22T04:31:51Z | 2006-06-22T04:31:51Z | Predictions as statements and decisions | Prediction is a complex notion, and different predictors (such as people, computer programs, and probabilistic theories) can pursue very different goals. In this paper I will review some popular kinds of prediction and argue that the theory of competitive on-line learning can benefit from the kinds of prediction that a... | [
"['Vladimir Vovk']"
] |
null | null | 0606100 | null | null | http://arxiv.org/pdf/cs/0606100v4 | 2011-10-11T10:21:45Z | 2006-06-23T10:19:40Z | The generating function of the polytope of transport matrices $U(r,c)$
as a positive semidefinite kernel of the marginals $r$ and $c$ | This paper has been withdrawn by the author due to a crucial error in the proof of Lemma 5. | [
"['Marco Cuturi']"
] |
null | null | 0606315 | null | null | http://arxiv.org/pdf/math/0606315v1 | 2006-06-13T17:05:02Z | 2006-06-13T17:05:02Z | Bayesian Regression of Piecewise Constant Functions | We derive an exact and efficient Bayesian regression algorithm for piecewise constant functions of unknown segment number, boundary location, and levels. It works for any noise and segment level prior, e.g. Cauchy which can handle outliers. We derive simple but good estimates for the in-segment variance. We also propos... | [
"['Marcus Hutter']"
] |
null | null | 0606643 | null | null | http://arxiv.org/pdf/math/0606643v3 | 2006-07-18T05:18:07Z | 2006-06-26T13:03:11Z | Entropy And Vision | In vector quantization the number of vectors used to construct the codebook is always an undefined problem, there is always a compromise between the number of vectors and the quantity of information lost during the compression. In this text we present a minimum of Entropy principle that gives solution to this compromis... | [
"['Rami Kanhouche']"
] |
null | null | 0607047 | null | null | http://arxiv.org/pdf/cs/0607047v1 | 2006-07-11T13:52:39Z | 2006-07-11T13:52:39Z | PAC Classification based on PAC Estimates of Label Class Distributions | A standard approach in pattern classification is to estimate the distributions of the label classes, and then to apply the Bayes classifier to the estimates of the distributions in order to classify unlabeled examples. As one might expect, the better our estimates of the label class distributions, the better the result... | [
"['Nick Palmer' 'Paul W. Goldberg']"
] |
null | null | 0607067 | null | null | http://arxiv.org/pdf/cs/0607067v1 | 2006-07-13T15:52:04Z | 2006-07-13T15:52:04Z | Competing with stationary prediction strategies | In this paper we introduce the class of stationary prediction strategies and construct a prediction algorithm that asymptotically performs as well as the best continuous stationary strategy. We make mild compactness assumptions but no stochastic assumptions about the environment. In particular, no assumption of station... | [
"['Vladimir Vovk']"
] |
null | null | 0607085 | null | null | http://arxiv.org/pdf/cs/0607085v2 | 2008-11-07T16:21:18Z | 2006-07-18T07:21:51Z | Using Pseudo-Stochastic Rational Languages in Probabilistic Grammatical
Inference | In probabilistic grammatical inference, a usual goal is to infer a good approximation of an unknown distribution P called a stochastic language. The estimate of P stands in some class of probabilistic models such as probabilistic automata (PA). In this paper, we focus on probabilistic models based on multiplicity autom... | [
"['Amaury Habrard' 'Francois Denis' 'Yann Esposito']"
] |
null | null | 0607096 | null | null | http://arxiv.org/pdf/cs/0607096v1 | 2006-07-20T14:52:08Z | 2006-07-20T14:52:08Z | Logical settings for concept learning from incomplete examples in First
Order Logic | We investigate here concept learning from incomplete examples. Our first purpose is to discuss to what extent logical learning settings have to be modified in order to cope with data incompleteness. More precisely we are interested in extending the learning from interpretations setting introduced by L. De Raedt that ex... | [
"['Dominique Bouthinon' 'Henry Soldano' 'Véronique Ventos']"
] |
null | null | 0607110 | null | null | http://arxiv.org/pdf/cs/0607110v1 | 2006-07-25T15:57:56Z | 2006-07-25T15:57:56Z | A Theory of Probabilistic Boosting, Decision Trees and Matryoshki | We present a theory of boosting probabilistic classifiers. We place ourselves in the situation of a user who only provides a stopping parameter and a probabilistic weak learner/classifier and compare three types of boosting algorithms: probabilistic Adaboost, decision tree, and tree of trees of ... of trees, which we c... | [
"['Etienne Grossmann']"
] |
null | null | 0607120 | null | null | http://arxiv.org/pdf/cs/0607120v1 | 2006-07-27T18:23:45Z | 2006-07-27T18:23:45Z | Expressing Implicit Semantic Relations without Supervision | We present an unsupervised learning algorithm that mines large text corpora for patterns that express implicit semantic relations. For a given input word pair X:Y with some unspecified semantic relations, the corresponding output list of patterns <P1,...,Pm> is ranked according to how well each pattern Pi expresses the... | [
"['Peter D. Turney']"
] |
null | null | 0607134 | null | null | http://arxiv.org/pdf/cs/0607134v1 | 2006-07-27T22:11:07Z | 2006-07-27T22:11:07Z | Leading strategies in competitive on-line prediction | We start from a simple asymptotic result for the problem of on-line regression with the quadratic loss function: the class of continuous limited-memory prediction strategies admits a "leading prediction strategy", which not only asymptotically performs at least as well as any continuous limited-memory strategy but also... | [
"['Vladimir Vovk']"
] |
null | null | 0607136 | null | null | http://arxiv.org/pdf/cs/0607136v1 | 2006-07-28T21:45:41Z | 2006-07-28T21:45:41Z | Competing with Markov prediction strategies | Assuming that the loss function is convex in the prediction, we construct a prediction strategy universal for the class of Markov prediction strategies, not necessarily continuous. Allowing randomization, we remove the requirement of convexity. | [
"['Vladimir Vovk']"
] |
null | null | 0607138 | null | null | http://arxiv.org/pdf/cs/0607138v1 | 2006-07-30T10:44:48Z | 2006-07-30T10:44:48Z | A Foundation to Perception Computing, Logic and Automata | In this report, a novel approach to intelligence and learning is introduced, this approach is based on what we call 'perception logic'. Based on this logic, a computing mechanism and automata are introduced. Multi-resolution analysis of perceptual information is given, in which learning is accomplished in at most O(log... | [
"['Mohamed A. Belal']"
] |
null | null | 0608033 | null | null | http://arxiv.org/pdf/cs/0608033v1 | 2006-08-06T16:10:05Z | 2006-08-06T16:10:05Z | A Study on Learnability for Rigid Lambek Grammars | We present basic notions of Gold's "learnability in the limit" paradigm, first presented in 1967, a formalization of the cognitive process by which a native speaker gets to grasp the underlying grammar of his/her own native language by being exposed to well formed sentences generated by that grammar. Then we present La... | [
"['Roberto Bonato']"
] |
null | null | 0608100 | null | null | http://arxiv.org/abs/cs/0608100v1 | 2006-08-25T14:35:11Z | 2006-08-25T14:35:11Z | Similarity of Semantic Relations | There are at least two kinds of similarity. Relational similarity is correspondence between relations, in contrast with attributional similarity, which is correspondence between attributes. When two words have a high degree of attributional similarity, we call them synonyms. When two pairs of words have a high degree o... | [
"['Peter D. Turney']"
] |
null | null | 0608522 | null | null | http://arxiv.org/pdf/math/0608522v2 | 2007-06-27T08:23:07Z | 2006-08-21T18:35:42Z | Graph Laplacians and their convergence on random neighborhood graphs | Given a sample from a probability measure with support on a submanifold in Euclidean space one can construct a neighborhood graph which can be seen as an approximation of the submanifold. The graph Laplacian of such a graph is used in several machine learning methods like semi-supervised learning, dimensionality reduct... | [
"['Matthias Hein' 'Jean-Yves Audibert' 'Ulrike von Luxburg']"
] |
null | null | 0608713 | null | null | http://arxiv.org/pdf/math/0608713v1 | 2006-08-29T12:35:53Z | 2006-08-29T12:35:53Z | Occam's hammer: a link between randomized learning and multiple testing
FDR control | We establish a generic theoretical tool to construct probabilistic bounds for algorithms where the output is a subset of objects from an initial pool of candidates (or more generally, a probability distribution on said pool). This general device, dubbed "Occam's hammer'', acts as a meta layer when a probabilistic bound... | [
"['Gilles Blanchard' 'François Fleuret']"
] |
null | null | 0609007 | null | null | http://arxiv.org/pdf/cs/0609007v1 | 2006-09-03T21:30:03Z | 2006-09-03T21:30:03Z | A Massive Local Rules Search Approach to the Classification Problem | An approach to the classification problem of machine learning, based on building local classification rules, is developed. The local rules are considered as projections of the global classification rules to the event we want to classify. A massive global optimization algorithm is used for optimization of quality criter... | [
"['Vladislav Malyshkin' 'Ray Bakhramov' 'Andrey Gorodetsky']"
] |
null | null | 0609045 | null | null | http://arxiv.org/pdf/cs/0609045v1 | 2006-09-09T11:31:01Z | 2006-09-09T11:31:01Z | Metric entropy in competitive on-line prediction | Competitive on-line prediction (also known as universal prediction of individual sequences) is a strand of learning theory avoiding making any stochastic assumptions about the way the observations are generated. The predictor's goal is to compete with a benchmark class of prediction rules, which is often a proper Banac... | [
"['Vladimir Vovk']"
] |
null | null | 0609049 | null | null | http://arxiv.org/pdf/cs/0609049v2 | 2007-05-08T07:34:29Z | 2006-09-11T09:35:57Z | Scanning and Sequential Decision Making for Multi-Dimensional Data -
Part I: the Noiseless Case | We investigate the problem of scanning and prediction ("scandiction", for short) of multidimensional data arrays. This problem arises in several aspects of image and video processing, such as predictive coding, for example, where an image is compressed by coding the error sequence resulting from scandicting it. Thus, i... | [
"['Asaf Cohen' 'Neri Merhav' 'Tsachy Weissman']"
] |
null | null | 0609071 | null | null | http://arxiv.org/pdf/cs/0609071v2 | 2007-02-14T06:51:03Z | 2006-09-13T03:44:08Z | A kernel method for canonical correlation analysis | Canonical correlation analysis is a technique to extract common features from a pair of multivariate data. In complex situations, however, it does not extract useful features because of its linearity. On the other hand, kernel method used in support vector machine is an efficient approach to improve such a linear metho... | [
"['Shotaro Akaho']"
] |
null | null | 0609093 | null | null | http://arxiv.org/pdf/cs/0609093v1 | 2006-09-16T14:43:27Z | 2006-09-16T14:43:27Z | PAC Learning Mixtures of Axis-Aligned Gaussians with No Separation
Assumption | We propose and analyze a new vantage point for the learning of mixtures of Gaussians: namely, the PAC-style model of learning probability distributions introduced by Kearns et al. Here the task is to construct a hypothesis mixture of Gaussians that is statistically indistinguishable from the actual mixture generating t... | [
"['Jon Feldman' \"Ryan O'Donnell\" 'Rocco A. Servedio']"
] |
null | null | 0609140 | null | null | http://arxiv.org/pdf/cs/0609140v2 | 2008-10-15T14:08:17Z | 2006-09-25T19:06:59Z | Motion Primitives for Robotic Flight Control | We introduce a simple framework for learning aggressive maneuvers in flight control of UAVs. Having inspired from biological environment, dynamic movement primitives are analyzed and extended using nonlinear contraction theory. Accordingly, primitives of an observed movement are stably combined and concatenated. We dem... | [
"['Baris E. Perk' 'J. J. E. Slotine']"
] |
null | null | 0609153 | null | null | http://arxiv.org/pdf/cs/0609153v1 | 2006-09-27T18:42:44Z | 2006-09-27T18:42:44Z | Mining Generalized Graph Patterns based on User Examples | There has been a lot of recent interest in mining patterns from graphs. Often, the exact structure of the patterns of interest is not known. This happens, for example, when molecular structures are mined to discover fragments useful as features in chemical compound classification task, or when web sites are mined to di... | [
"['Pavel Dmitriev' 'Carl Lagoze']"
] |
null | null | 0609461 | null | null | http://arxiv.org/pdf/math/0609461v1 | 2006-09-16T07:00:36Z | 2006-09-16T07:00:36Z | Cross-Entropy method: convergence issues for extended implementation | The cross-entropy method (CE) developed by R. Rubinstein is an elegant practical principle for simulating rare events. The method approximates the probability of the rare event by means of a family of probabilistic models. The method has been extended to optimization, by considering an optimal event as a rare event. CE... | [
"['Frederic Dambreville']"
] |
null | null | 0610033 | null | null | http://arxiv.org/abs/cs/0610033v1 | 2006-10-06T04:45:32Z | 2006-10-06T04:45:32Z | A kernel for time series based on global alignments | We propose in this paper a new family of kernels to handle times series, notably speech data, within the framework of kernel methods which includes popular algorithms such as the Support Vector Machine. These kernels elaborate on the well known Dynamic Time Warping (DTW) family of distances by considering the same set ... | [
"['Marco Cuturi' 'Jean-Philippe Vert' 'Oystein Birkenes' 'Tomoko Matsui']"
] |
null | null | 0610040 | null | null | http://arxiv.org/pdf/q-bio/0610040v1 | 2006-10-21T06:33:24Z | 2006-10-21T06:33:24Z | Metric learning pairwise kernel for graph inference | Much recent work in bioinformatics has focused on the inference of various types of biological networks, representing gene regulation, metabolic processes, protein-protein interactions, etc. A common setting involves inferring network edges in a supervised fashion from a set of high-confidence edges, possibly character... | [
"['Jean-Philippe Vert' 'Jian Qiu' 'William Stafford Noble']"
] |
null | null | 0610051 | null | null | http://arxiv.org/abs/physics/0610051v1 | 2006-10-09T18:41:57Z | 2006-10-09T18:41:57Z | Structural Inference of Hierarchies in Networks | One property of networks that has received comparatively little attention is hierarchy, i.e., the property of having vertices that cluster together in groups, which then join to form groups of groups, and so forth, up through all levels of organization in the network. Here, we give a precise definition of hierarchical ... | [
"['Aaron Clauset' 'Cristopher Moore' 'M. E. J. Newman']"
] |
null | null | 0610126 | null | null | http://arxiv.org/abs/cs/0610126v1 | 2006-10-20T16:37:11Z | 2006-10-20T16:37:11Z | Fitness Uniform Optimization | 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' 'Shane Legg']"
] |
null | null | 0610155 | null | null | http://arxiv.org/pdf/cs/0610155v1 | 2006-10-27T07:08:51Z | 2006-10-27T07:08:51Z | Nonlinear Estimators and Tail Bounds for Dimension Reduction in $l_1$
Using Cauchy Random Projections | For dimension reduction in $l_1$, the method of {em Cauchy random projections} multiplies the original data matrix $mathbf{A} inmathbb{R}^{ntimes D}$ with a random matrix $mathbf{R} in mathbb{R}^{Dtimes k}$ ($kllmin(n,D)$) whose entries are i.i.d. samples of the standard Cauchy C(0,1). Because of the impossibility resu... | [
"['Ping Li' 'Trevor J. Hastie' 'Kenneth W. Church']"
] |
null | null | 0610158 | null | null | http://arxiv.org/pdf/cs/0610158v1 | 2006-10-27T16:02:34Z | 2006-10-27T16:02:34Z | Considering users' behaviours in improving the responses of an
information base | In this paper, our aim is to propose a model that helps in the efficient use of an information system by users, within the organization represented by the IS, in order to resolve their decisional problems. In other words we want to aid the user within an organization in obtaining the information that corresponds to his... | [
"['Babajide Afolabi' 'Odile Thiery']"
] |
null | null | 0610170 | null | null | http://arxiv.org/pdf/cs/0610170v1 | 2006-10-30T16:44:58Z | 2006-10-30T16:44:58Z | Low-complexity modular policies: learning to play Pac-Man and a new
framework beyond MDPs | In this paper we propose a method that learns to play Pac-Man. We define a set of high-level observation and action modules. Actions are temporally extended, and multiple action modules may be in effect concurrently. A decision of the agent is represented as a rule-based policy. For learning, we apply the cross-entropy... | [
"['Istvan Szita' 'Andras Lorincz']"
] |
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