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Statistical learning theory
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TLDR
Presenting a method for determining the necessary and sufficient conditions for consistency of learning process, the author covers function estimates from small data pools, applying these estimations to real-life problems, and much more.Abstract:
A comprehensive look at learning and generalization theory. The statistical theory of learning and generalization concerns the problem of choosing desired functions on the basis of empirical data. Highly applicable to a variety of computer science and robotics fields, this book offers lucid coverage of the theory as a whole. Presenting a method for determining the necessary and sufficient conditions for consistency of learning process, the author covers function estimates from small data pools, applying these estimations to real-life problems, and much more.read more
Citations
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Journal ArticleDOI
Nontechnical Loss Detection for Metered Customers in Power Utility Using Support Vector Machines
TL;DR: In this article, the authors presented a new approach towards nontechnical loss (NTL) detection in power utilities using an artificial intelligence based technique, support vector machine (SVM), which provided a method of data mining, which involves feature extraction from historical customer consumption data.
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Statistical edge detection: learning and evaluating edge cues
TL;DR: This work uses presegmented images to learn the probability distributions of filter responses conditioned on whether they are evaluated on or off an edge, and evaluates the effectiveness of different visual cues using the Chernoff information and Receiver Operator Characteristic (ROC) curves.
Journal ArticleDOI
Kernel method for nonlinear granger causality.
Daniele Marinazzo,Daniele Marinazzo,M. Pellicoro,M. Pellicoro,Sebastiano Stramaglia,Sebastiano Stramaglia +5 more
TL;DR: The method performs linear Granger causality in the feature space of suitable kernel functions, assuming arbitrary degree of nonlinearity, and develops a new strategy to cope with the problem of overfitting, based on the geometry of reproducing kernel Hilbert spaces.
Journal ArticleDOI
Iterative RELIEF for Feature Weighting: Algorithms, Theories, and Applications
TL;DR: This paper proposes an iterative RELIEF (I-RELIEF) algorithm to alleviate the deficiencies of RELIEf by exploring the framework of the expectation-maximization algorithm.
Journal ArticleDOI
Protein-ligand interaction prediction
Laurent Jacob,Jean-Philippe Vert +1 more
TL;DR: Following the recent chemogenomics trend, this work adopts a cross-target view and attempts to screen the chemical space against whole families of proteins simultaneously, and reports dramatic improvements in prediction accuracy over classical ligand-based virtual screening, in particular for targets with few or no known ligands.
References
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Book
The Nature of Statistical Learning Theory
TL;DR: Setting of the learning problem consistency of learning processes bounds on the rate of convergence ofLearning processes controlling the generalization ability of learning process constructing learning algorithms what is important in learning theory?