An Elementary Introduction to Statistical Learning Theory

by Sanjeev Kulkarni, Gilbert Harman

A joint endeavor from leading researchers in the fields of philosophy and electrical engineering, "An Introduction to Statistical Learning Theory" provides a broad and accessible introduction to rapidly evolving field of statistical pattern recognition and statistical learning theory. Exploring topics that are not often covered in introductory level books on statistical learning theory, including PAC learning, VC dimension, and simplicity, the authors present upper-undergraduate and graduate levels with the basic theory behind contemporary machine learning and uniquely suggest it serves as an excellent framework for philosophical thinking about inductive inference.

235 pages, Kindle Edition

First published January 1, 2011

Reviews

[Jin Shusong · Rating 4 out of 5]

This book does not introduce the algorithms in details. However, the author really explain the concepts of machine learning, especially the ideas of those popular algorithms very clearly. As an elementary book in machine learning, the book is very valuable. After reading this book, one will probably understand those algorithms and the underlying math more quickly.

[Hunan Rostomyan · Rating 4 out of 5]

Accessible introduction to machine learning. Has a bunch of typos and inaccuracies, but still worth a read.

[Manuel Brenner · Rating 4 out of 5]

Well-written, accessible introductory book focusing on conceptual aspects of classification and learning theory.

[Delhi Irc]

Location: ND6 IRC
Accession No: DL030723