Generalization Theory

Statistical Learning Theory by Vladimir N. Vapnik [Repost]  

Posted by tanas.olesya at Nov. 25, 2014
Statistical Learning Theory by Vladimir N. Vapnik [Repost]

Statistical Learning Theory by Vladimir N. Vapnik
English | September 30, 1998 | ISBN: 0471030031 | 740 pages | PDF | 26 MB

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.

Vladimir N. Vapnik - Statistical Learning Theory  

Posted by danrop at June 26, 2007
Vladimir N. Vapnik - Statistical Learning Theory

Vladimir N. Vapnik, "Statistical Learning Theory"
John Wiley & Sons | ISBN : 0471030031 | Year - 1998 | DjVu | 5.8 MB | 732 Pages

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.

The Nature of Statistical Learning Theory [Repost]  eBooks & eLearning

Posted by tanas.olesya at July 8, 2016
The Nature of Statistical Learning Theory  [Repost]

The Nature of Statistical Learning Theory by Vladimir Vapnik
English | 19 Nov. 1999 | ISBN: 0387987800 | 334 Pages | PDF | 10 MB

The aim of this book is to discuss the fundamental ideas which lie behind the statistical theory of learning and generalization.

Theory of Holors: A Generalization of Tensors by Domina Eberle Spencer [Repost]  eBooks & eLearning

Posted by AlenMiler at Dec. 28, 2015
Theory of Holors: A Generalization of Tensors by Domina Eberle Spencer [Repost]

Theory of Holors: A Generalization of Tensors by Domina Eberle Spencer
English | June 27, 1986 | ISBN: 0521245850, 0521019001 | 412 Pages | PDF | 67 MB

The word holor is a term coined by the authors to describe a mathematical entity that is made up of one or more independent quantities, and includes complex numbers, scalars, vectors, matrices, tensors, quaternions, and other hypernumbers.
Theory of Holors: A Generalization of Tensors by Domina Eberle Spencer

Theory of Holors: A Generalization of Tensors by Domina Eberle Spencer
English | June 27, 1986 | ISBN: 0521245850, 0521019001 | 412 Pages | PDF | 67 MB

The word holor is a term coined by the authors to describe a mathematical entity that is made up of one or more independent quantities, and includes complex numbers, scalars, vectors, matrices, tensors, quaternions, and other hypernumbers.
The Nature of Statistical Learning Theory by Vladimir Vapnik [Repost]

The Nature of Statistical Learning Theory (Information Science and Statistics) by Vladimir Vapnik
English | Nov 19, 1999 | ISBN: 0387987800 | 334 Pages | PDF | 10 MB

The aim of this book is to discuss the fundamental ideas which lie behind the statistical theory of learning and generalization. It considers learning as a general problem of function estimation based on empirical data.
The Nature of Statistical Learning Theory by Vladimir Vapnik [Repost]

The Nature of Statistical Learning Theory (Information Science and Statistics) by Vladimir Vapnik
Springer; 2nd edition | November 19, 1999 | English | ISBN: 0387987800 | 334 pages | PDF | 10 MB

The aim of this book is to discuss the fundamental ideas which lie behind the statistical theory of learning and generalization. It considers learning as a general problem 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.

Applications of q-Calculus in Operator Theory (repost)  

Posted by interes at July 15, 2014
Applications of q-Calculus in Operator Theory (repost)

Applications of q-Calculus in Operator Theory by Ali ARAL, Vijay Gupta and Ravi P. Agarwal
English | ISBN: 1461469457 | 2013 | 265 pages | PDF | 1,7 MB

The approximation of functions by linear positive operators is an important research topic in general mathematics and it also provides powerful tools to application areas such as computer-aided geometric design, numerical analysis, and solutions of differential equations. q-Calculus is a generalization of many subjects, such as hypergeometric series, complex analysis, and particle physics.

Applications of q-Calculus in Operator Theory (repost)  

Posted by ph4rr3l at Oct. 29, 2013
Applications of q-Calculus in Operator Theory (repost)

Ali Aral, "Applications of q-Calculus in Operator Theory"
English | ISBN: 1461469457 | 2013 | 265 pages | PDF | 3 MB

The approximation of functions by linear positive operators is an important research topic in general mathematics and it also provides powerful tools to application areas such as computer-aided geometric design, numerical analysis, and solutions of differential equations. q-Calculus is a generalization of many subjects, such as hypergeometric series, complex analysis, and particle physics. ​​This monograph is an introduction to combining approximation theory and q-Calculus with applications, by using well- known operators. The presentation is systematic and the authors include a brief summary of the notations and basic definitions of q-calculus before delving into more advanced material. The many applications of q-calculus in the theory of approximation, especially on various operators, which includes convergence of operators to functions in real and complex domain​ forms the gist of the book.
Vladimir Vapnik, The Nature of Statistical Learning Theory (Repost)

Vladimir Vapnik, The Nature of Statistical Learning Theory
ISBN: 0387987800 | edition 1999 | PDF | 334 pages | 10 mb

The aim of this book is to discuss the fundamental ideas which lie behind the statistical theory of learning and generalization. It considers learning as a general problem 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.