Statistical Learning Theory
Yuhai Wu, Vladimir Vapnik
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.
Topics & Concepts
GeneralizationStatistical learning theoryArtificial intelligenceConsistency (knowledge bases)Computer scienceMachine learningStatistical theoryVariety (cybernetics)Process (computing)Algorithmic learning theoryLearning theoryFunction (biology)MathematicsUnsupervised learningStatisticsMathematics educationMathematical analysisSupport vector machineBiologyEvolutionary biologyOperating systemEvolutionary Algorithms and ApplicationsMetaheuristic Optimization Algorithms ResearchNeural Networks and Applications