The nature of statistical learning theory pdf download

Back Matter Pages About this book Introduction The aim of this book is to discuss the fundamental ideas which lie behind the statistical theory of learning and generalization. It considers learning from the general point of view of function estimation based on empirical data. Omitting proofs and technical details, the author concentrates on discussing the main results of learning theory and their connections to fundamental problems in statistics. These include: - the general setting of learning problems and the general model of minimizing the risk functional from empirical data - a comprehensive analysis of the empirical risk minimization principle and shows how this allows for the construction of necessary and sufficient conditions for consistency - non-asymptotic bounds for the risk achieved using the empirical risk minimization principle - principles for controlling the generalization ability of learning machines using small sample sizes - introducing a new type of universal learning machine that controls the generalization ability.

Statistica algorithms boundary element method construction controlling convergence function functional learning learning algorithm learning theory model proof statistical theory statistics. Authors and affiliations Vladimir N.

Highly Influenced. View 4 excerpts, cites background. Statistical learning theory and state of the art in SVM. View 1 excerpt, cites results. Learning from examples has been traditionally based on correlation or on the mean square error MSE criterion, in spite of the fact that learning is intrinsically related with the extraction of … Expand.

View 1 excerpt, cites methods. A Simple Theory of Scientific Learning. Scientists use diverse evidence to learn about the relative validity of various broad theories. Given the lack of statistical structure in this scientific learning problem, techniques of model … Expand.

View 3 excerpts, cites methods and background. Kernels and Ensembles : Perspectives on Statistical Learning.

Since their emergence in the s, the support vector machine and the AdaBoost algorithm have spawned a wave of research in statistical machine learning. Much of this new research falls into one of … Expand. View 1 excerpt, cites background. Enclosing Machine Learning. Authors view affiliations Vladimir N. It considers learning as a general problem of function estimation based on empirical data.

Front Matter Pages i-xix. Pages Setting of the Learning Problem. Consistency of Learning Processes. Bounds on the Rate of Convergence of Learning Processes. Controlling the Generalization Ability of Learning Processes. Methods of Pattern Recognition. Methods of Function Estimation.