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Title: Learning from Scarce Experience Abstract: Searching the space of policies directly for the optimal policy has been one popular method for solving partially observable reinforcement learning problems. Typically, with each change of the target policy, its value is estimated from the results of following that very ... |
Title: Required sample size for learning sparse Bayesian networks with many variables Abstract: Learning joint probability distributions on n random variables requires exponential sample size in the generic case. Here we consider the case that a temporal (or causal) order of the variables is known and that the (unknown... |
Title: Bootstrapping Structure into Language: Alignment-Based Learning Abstract: This thesis introduces a new unsupervised learning framework, called Alignment-Based Learning, which is based on the alignment of sentences and Harris's (1951) notion of substitutability. Instances of the framework can be applied to an unt... |
Title: Thumbs up? Sentiment Classification using Machine Learning Techniques Abstract: We consider the problem of classifying documents not by topic, but by overall sentiment, e.g., determining whether a review is positive or negative. Using movie reviews as data, we find that standard machine learning techniques defin... |
Title: Unsupervised Learning of Morphology without Morphemes Abstract: The first morphological learner based upon the theory of Whole Word Morphology Ford et al. (1997) is outlined, and preliminary evaluation results are presented. The program, Whole Word Morphologizer, takes a POS-tagged lexicon as input, induces morp... |
Title: Robust Feature Selection by Mutual Information Distributions Abstract: Mutual information is widely used in artificial intelligence, in a descriptive way, to measure the stochastic dependence of discrete random variables. In order to address questions such as the reliability of the empirical value, one must cons... |
Title: The Prioritized Inductive Logic Programs Abstract: The limit behavior of inductive logic programs has not been explored, but when considering incremental or online inductive learning algorithms which usually run ongoingly, such behavior of the programs should be taken into account. An example is given to show th... |
Title: Optimal Ordered Problem Solver Abstract: We present a novel, general, optimally fast, incremental way of searching for a universal algorithm that solves each task in a sequence of tasks. The Optimal Ordered Problem Solver (OOPS) continually organizes and exploits previously found solutions to earlier tasks, effi... |
Title: An Algorithm for Pattern Discovery in Time Series Abstract: We present a new algorithm for discovering patterns in time series and other sequential data. We exhibit a reliable procedure for building the minimal set of hidden, Markovian states that is statistically capable of producing the behavior exhibited in t... |
Title: Evaluation of the Performance of the Markov Blanket Bayesian Classifier Algorithm Abstract: The Markov Blanket Bayesian Classifier is a recently-proposed algorithm for construction of probabilistic classifiers. This paper presents an empirical comparison of the MBBC algorithm with three other Bayesian classifier... |
Title: Maximing the Margin in the Input Space Abstract: We propose a novel criterion for support vector machine learning: maximizing the margin in the input space, not in the feature (Hilbert) space. This criterion is a discriminative version of the principal curve proposed by Hastie et al. The criterion is appropriate... |
Title: Approximating Incomplete Kernel Matrices by the em Algorithm Abstract: In biological data, it is often the case that observed data are available only for a subset of samples. When a kernel matrix is derived from such data, we have to leave the entries for unavailable samples as missing. In this paper, we make us... |
Title: Principal Manifolds and Nonlinear Dimension Reduction via Local Tangent Space Alignment Abstract: Nonlinear manifold learning from unorganized data points is a very challenging unsupervised learning and data visualization problem with a great variety of applications. In this paper we present a new algorithm for ... |
Title: Mining the Web for Lexical Knowledge to Improve Keyphrase Extraction: Learning from Labeled and Unlabeled Data Abstract: Keyphrases are useful for a variety of purposes, including summarizing, indexing, labeling, categorizing, clustering, highlighting, browsing, and searching. The task of automatic keyphrase ext... |
Title: Unsupervised Learning of Semantic Orientation from a Hundred-Billion-Word Corpus Abstract: The evaluative character of a word is called its semantic orientation. A positive semantic orientation implies desirability (e.g., "honest", "intrepid") and a negative semantic orientation implies undesirability (e.g., "di... |
Title: Learning to Extract Keyphrases from Text Abstract: Many academic journals ask their authors to provide a list of about five to fifteen key words, to appear on the first page of each article. Since these key words are often phrases of two or more words, we prefer to call them keyphrases. There is a surprisingly w... |
Title: Extraction of Keyphrases from Text: Evaluation of Four Algorithms Abstract: This report presents an empirical evaluation of four algorithms for automatically extracting keywords and keyphrases from documents. The four algorithms are compared using five different collections of documents. For each document, we ha... |
Title: Learning Algorithms for Keyphrase Extraction Abstract: Many academic journals ask their authors to provide a list of about five to fifteen keywords, to appear on the first page of each article. Since these key words are often phrases of two or more words, we prefer to call them keyphrases. There is a wide variet... |
Title: How to Shift Bias: Lessons from the Baldwin Effect Abstract: An inductive learning algorithm takes a set of data as input and generates a hypothesis as output. A set of data is typically consistent with an infinite number of hypotheses; therefore, there must be factors other than the data that determine the outp... |
Title: Unsupervised Language Acquisition: Theory and Practice Abstract: In this thesis I present various algorithms for the unsupervised machine learning of aspects of natural languages using a variety of statistical models. The scientific object of the work is to examine the validity of the so-called Argument from the... |
Title: Technical Note: Bias and the Quantification of Stability Abstract: Research on bias in machine learning algorithms has generally been concerned with the impact of bias on predictive accuracy. We believe that there are other factors that should also play a role in the evaluation of bias. One such factor is the st... |
Title: A Theory of Cross-Validation Error Abstract: This paper presents a theory of error in cross-validation testing of algorithms for predicting real-valued attributes. The theory justifies the claim that predicting real-valued attributes requires balancing the conflicting demands of simplicity and accuracy. Furtherm... |
Title: Theoretical Analyses of Cross-Validation Error and Voting in Instance-Based Learning Abstract: This paper begins with a general theory of error in cross-validation testing of algorithms for supervised learning from examples. It is assumed that the examples are described by attribute-value pairs, where the values... |
Title: Contextual Normalization Applied to Aircraft Gas Turbine Engine Diagnosis Abstract: Diagnosing faults in aircraft gas turbine engines is a complex problem. It involves several tasks, including rapid and accurate interpretation of patterns in engine sensor data. We have investigated contextual normalization for t... |
Title: Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews Abstract: This paper presents a simple unsupervised learning algorithm for classifying reviews as recommended (thumbs up) or not recommended (thumbs down). The classification of a review is predicted by the average s... |
Title: Mining the Web for Synonyms: PMI-IR versus LSA on TOEFL Abstract: This paper presents a simple unsupervised learning algorithm for recognizing synonyms, based on statistical data acquired by querying a Web search engine. The algorithm, called PMI-IR, uses Pointwise Mutual Information (PMI) and Information Retrie... |
Title: Types of Cost in Inductive Concept Learning Abstract: Inductive concept learning is the task of learning to assign cases to a discrete set of classes. In real-world applications of concept learning, there are many different types of cost involved. The majority of the machine learning literature ignores all types... |
Title: Exploiting Context When Learning to Classify Abstract: This paper addresses the problem of classifying observations when features are context-sensitive, specifically when the testing set involves a context that is different from the training set. The paper begins with a precise definition of the problem, then ge... |
Title: Myths and Legends of the Baldwin Effect Abstract: This position paper argues that the Baldwin effect is widely misunderstood by the evolutionary computation community. The misunderstandings appear to fall into two general categories. Firstly, it is commonly believed that the Baldwin effect is concerned with the ... |
Title: The Management of Context-Sensitive Features: A Review of Strategies Abstract: In this paper, we review five heuristic strategies for handling context-sensitive features in supervised machine learning from examples. We discuss two methods for recovering lost (implicit) contextual information. We mention some evi... |
Title: The Identification of Context-Sensitive Features: A Formal Definition of Context for Concept Learning Abstract: A large body of research in machine learning is concerned with supervised learning from examples. The examples are typically represented as vectors in a multi-dimensional feature space (also known as a... |
Title: Low Size-Complexity Inductive Logic Programming: The East-West Challenge Considered as a Problem in Cost-Sensitive Classification Abstract: The Inductive Logic Programming community has considered proof-complexity and model-complexity, but, until recently, size-complexity has received little attention. Recently ... |
Title: Data Engineering for the Analysis of Semiconductor Manufacturing Data Abstract: 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... |
Title: Robust Classification with Context-Sensitive Features Abstract: 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, ... |
Title: Kalman filter control in the reinforcement learning framework Abstract: 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 of... |
Title: Convergence and Loss Bounds for Bayesian Sequence Prediction Abstract: 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 bel... |
Title: The New AI: General & Sound & Relevant for Physics Abstract: 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 fea... |
Title: Unsupervised Learning in a Framework of Information Compression by Multiple Alignment, Unification and Search Abstract: 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), de... |
Title: Algorithmic Clustering of Music Abstract: 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 area... |
Title: On the Existence and Convergence Computable Universal Priors Abstract: 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 pos... |
Title: Sequence Prediction based on Monotone Complexity Abstract: 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 we... |
Title: Universal Sequential Decisions in Unknown Environments Abstract: 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 s... |
Title: Reinforcement Learning with Linear Function Approximation and LQ control Converges Abstract: 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... |
Title: Bayesian Treatment of Incomplete Discrete Data applied to Mutual Information and Feature Selection Abstract: 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... |
Title: AWESOME: A General Multiagent Learning Algorithm that Converges in Self-Play and Learns a Best Response Against Stationary Opponents Abstract: 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-p... |
Title: BL-WoLF: A Framework For Loss-Bounded Learnability In Zero-Sum Games Abstract: 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... |
Title: Manifold Learning with Geodesic Minimal Spanning Trees Abstract: 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 c... |
Title: Learning Analogies and Semantic Relations Abstract: 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, me... |
Title: Controlled hierarchical filtering: Model of neocortical sensory processing Abstract: 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... |
Title: Coherent Keyphrase Extraction via Web Mining Abstract: 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 do... |
Title: Reliable and Efficient Inference of Bayesian Networks from Sparse Data by Statistical Learning Theory Abstract: 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.... |
Title: Using Simulated Annealing to Calculate the Trembles of Trembling Hand Perfection Abstract: 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 a... |
Title: Measuring Praise and Criticism: Inference of Semantic Orientation from Association Abstract: 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"... |
Title: Combining Independent Modules to Solve Multiple-choice Synonym and Analogy Problems Abstract: 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 resear... |
Title: Optimality of Universal Bayesian Sequence Prediction for General Loss and Alphabet Abstract: 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 ... |
Title: Toward Attribute Efficient Learning Algorithms Abstract: 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 $n^{\tilde{O}(k^{1/3})}$... |
Title: Hybrid LQG-Neural Controller for Inverted Pendulum System Abstract: 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 stipulate... |
Title: Improving spam filtering by combining Naive Bayes with simple k-nearest neighbor searches Abstract: 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 class... |
Title: Failure-Free Genetic Algorithm Optimization of a System Controller Using SAFE/LEARNING Controllers in Tandem Abstract: 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 cont... |
Title: Mapping Subsets of Scholarly Information Abstract: 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 ... |
Title: Acquiring Lexical Paraphrases from a Single Corpus Abstract: 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, ea... |
Title: Part-of-Speech Tagging with Minimal Lexicalization Abstract: 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 c... |
Title: About Unitary Rating Score Constructing Abstract: 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 ... |
Title: A Numerical Example on the Principles of Stochastic Discrimination Abstract: 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... |
Title: Fitness inheritance in the Bayesian optimization algorithm Abstract: 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 r... |
Title: Distribution of Mutual Information from Complete and Incomplete Data Abstract: 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... |
Title: Concept of E-machine: How does a "dynamical" brain learn to process "symbolic" information? Part I Abstract: 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 br... |
Title: Tournament versus Fitness Uniform Selection Abstract: 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... |
Title: When Do Differences Matter? On-Line Feature Extraction Through Cognitive Economy Abstract: 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;... |
Title: Convergence of Discrete MDL for Sequential Prediction Abstract: 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 sequenc... |
Title: Prediction with Expert Advice by Following the Perturbed Leader for General Weights Abstract: 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 derivative... |
Title: Knowledge Reduction and Discovery based on Demarcation Information Abstract: 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 cal... |
Title: Blind Construction of Optimal Nonlinear Recursive Predictors for Discrete Sequences Abstract: 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 Mar... |
Title: Learning for Adaptive Real-time Search Abstract: 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 comp... |
Title: On the Convergence Speed of MDL Predictions for Bernoulli Sequences Abstract: 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... |
Title: Universal Convergence of Semimeasures on Individual Random Sequences Abstract: 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 ... |
Title: Word Sense Disambiguation by Web Mining for Word Co-occurrence Probabilities Abstract: 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 machin... |
Title: Semantic Linking - a Context-Based Approach to Interactivity in Hypermedia Abstract: 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 inter... |
Title: Hypermedia Learning Objects System - On the Way to a Semantic Educational Web Abstract: 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 def... |
Title: Online convex optimization in the bandit setting: gradient descent without a gradient Abstract: 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... |
Title: Journal of New Democratic Methods: An Introduction Abstract: 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 t... |
Title: Non-negative matrix factorization with sparseness constraints Abstract: 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 p... |
Title: Applying Policy Iteration for Training Recurrent Neural Networks Abstract: 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 functio... |
Title: L1 regularization is better than L2 for learning and predicting chaotic systems Abstract: 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 emerg... |
Title: Automated Pattern Detection--An Algorithm for Constructing Optimally Synchronizing Multi-Regular Language Filters Abstract: 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 analysi... |
Title: Self-Organised Factorial Encoding of a Toroidal Manifold Abstract: 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 even... |
Title: Neural Architectures for Robot Intelligence Abstract: 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 w... |
Title: A Note on the PAC Bayesian Theorem Abstract: 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. |
Title: Mining Heterogeneous Multivariate Time-Series for Learning Meaningful Patterns: Application to Home Health Telecare Abstract: 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 tim... |
Title: Human-Level Performance on Word Analogy Questions by Latent Relational Analysis Abstract: 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 transl... |
Title: The Google Similarity Distance Abstract: 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... |
Title: Online Learning of Aggregate Knowledge about Non-linear Preferences Applied to Negotiating Prices and Bundles Abstract: 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, ... |
Title: Combining Independent Modules in Lexical Multiple-Choice Problems Abstract: 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 examinin... |
Title: An Empirical Study of MDL Model Selection with Infinite Parametric Complexity Abstract: 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 ... |
Title: Bandit Problems with Side Observations Abstract: 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 var... |
Title: Asymptotic Log-loss of Prequential Maximum Likelihood Codes Abstract: 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=... |
Title: Stability Analysis for Regularized Least Squares Regression Abstract: 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+ l... |
Title: Estimating mutual information and multi--information in large networks Abstract: 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 ... |
Title: Master Algorithms for Active Experts Problems based on Increasing Loss Values Abstract: 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 wit... |
Title: On sample complexity for computational pattern recognition Abstract: 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 suc... |
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