The Nature of Statistical Learning Theory (Information Science and Statistics) - Image 1

£179.99

Springer The Nature of Statistical Learning Theory (Information Science and Statistics)

Name: The Nature of Statistical Learning Theory (Information Science and Statistics)
Brand: Springer
Price: 179.99 GBP
Availability: InStock

AI & Machine Learning

Price History & Forecast

Last 7 days • 7 data points (No recent data available)

Historical

Generating forecast...

Price Distribution

Price distribution over 7 days • 1 price levels

Days at Price

Price Analysis

Most common price: £180 (7 days, 100.0%)

Price range: £180 - £180

Price levels: 1 different prices over 7 days

Description

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. These include: * the setting of learning problems based on the model of minimizing the risk functional from empirical data * a comprehensive analysis of the empirical risk minimization principle including necessary and sufficient conditions for its 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 based on these bounds * the Support Vector methods that control the generalization ability when estimating function using small sample size. The second edition of the book contains three new chapters devoted to further development of the learning theory and SVM techniques. These include: * the theory of direct method of learning based on solving multidimensional integral equations for density, conditional probability, and conditional density estimation * a new inductive principle of learning. Written in a readable and concise style, the book is intended for statisticians, mathematicians, physicists, and computer scientists. Vladimir N. Vapnik is Technology Leader AT&T Labs-Research and Professor of London University. He is one of the founders of

Product Specifications

Brand: Springer
Format: hardcover
ASIN: 0387987800
Category: Books > Subjects > Computing & Internet > Computer Science > AI & Machine Learning
Domain: Amazon UK
Release Date: 19 November 1999
Listed Since: 12 February 2007

Barcode

No barcode data available

Springer The Nature of Statistical Learning Theory (Information Science and Statistics)

Price History & Forecast

Price Distribution

Price Analysis

Description

Product Specifications

Barcode

Similar Products You Might Like

Machine Learning: A Practical Approach on the Statistical Learning Theory

An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)

An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)

An Elementary Introduction to Statistical Learning Theory (Wiley Series in Probability and Statistics)

The Nature of Statistical Learning Theory (Information Science and Statistics)

Statistical Learning from a Regression Perspective (Springer Texts in Statistics)

Statistical Learning from a Regression Perspective (Springer Texts in Statistics)

The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)

Information and Complexity in Statistical Modeling (Information Science and Statistics)

Machine Learning: A Bayesian and Optimization Perspective

Applied Machine Learning

Wiley Statistical Learning Theory - Adaptive and Cognitive Systems

Probability for Statistics and Machine Learning: Fundamentals and Advanced Topics (Springer Texts in Statistics)

Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach

Probability and Statistics for Machine Learning: A Textbook

Foundations of Deep Learning (Machine Learning: Foundations, Methodologies, and Applications)

Neural Networks and Statistical Learning

Neural Networks and Statistical Learning

Data Science and Machine Learning: Mathematical and Statistical Methods, Second Edition (Chapman & Hall/CRC Machine Learning & Pattern Recognition)

Sampling Techniques for Supervised or Unsupervised Tasks (Unsupervised and Semi-Supervised Learning)

Machine Learning: An Algorithmic Perspective, Second Edition

Foundations of Machine Learning (Adaptive Computation and Machine Learning series)

Neural Networks and Deep Learning: A Textbook

Statistical Machine Learning: A Unified Framework (Chapman & Hall/CRC Texts in Statistical Science)