Statistical Learning Theory
| Author | : | |
| Rating | : | 4.52 (786 Votes) |
| Asin | : | 0471030031 |
| Format Type | : | paperback |
| Number of Pages | : | 768 Pages |
| Publish Date | : | 2016-02-19 |
| Language | : | English |
DESCRIPTION:
From the Publisher This book is devoted to the statistical theory of learning and generalization, that is, the problem of choosing the desired function on the basis of empirical data. . The author will present the whole picture of learning and generalization theory. Learning theory has applications in many fields, such as psychology, education and computer science
An excellent overview The field of statistical learning theory has not only seen considerable advances in the last fifteen years, it has also found many applications, some of these appearing in commercial packages. It is now classified as a subfield of artificial intelligence, and as such gives an alternative, and frequently more general viewpoint on such topics as pattern recognition, regression estimation, and signal processing. The author of this book is one of the originators of statistical learning theory, and has written a book that will give the mathematically sop. Michael R. Chernick said statistical learning based on the VC class. Vapnik and Chernovenkis extended the Glivenko-Cantelli Theorem in their work on classification and statistical learning. Vapnik in recent texts has described a form of nonparametric statistical inference based on approximating functions and the Vapnik-Chernovenkis dimension.In an earlier book published by Springer-Verlag he develops the basics of the theory. However to keep the mathematical level excessible to computer scientists and engineers he avoided the mathematical proofs needed for mathematical rigor. This text is an advanced text that provid. In case you find it difficult to read In case you don't think yourself have a strong background in probability theory, I would recommend the book by Ralf Herbrich "Learning Kernel Classifiers". This book seems hard to read in the beginning because of heavy mathematical notation. It is quite easy to follow when you drink some ice cold water and calm down. Especially noteworthy is the derivation of VC-dimension based bounds, which is the few book/papers I read that explain how those strange equations are obtained. In addition, the book "Kernel Methods for Pattern Analysis by Nello Cristia
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.. A comprehensive look at learning and generalization theory. Highly applicable to a variety of computer science and robotics fields, this book offers lucid coverage of the theory as a whole. The statistical theory of learning and generalization concerns the problem of choosing desired functions on the basis of empirical data