A thought-provoking look at statistical learning theory and its
role in understanding human learning and inductive
reasoning
A joint endeavor from leading researchers in the fields of
philosophy and electrical engineering, An Elementary
Introduction to Statistical Learning Theory is a comprehensive
and accessible primer on the rapidly evolving fields of statistical
pattern recognition and statistical learning theory. Explaining
these areas at a level and in a way that is not often found in
other books on the topic, the authors present the basic theory
behind contemporary machine learning and uniquely utilize its
foundations as a framework for philosophical thinking about
inductive inference.
Promoting the fundamental goal of statistical learning, knowing
what is achievable and what is not, this book demonstrates the
value of a systematic methodology when used along with the needed
techniques for evaluating the performance of a learning system.
First, an introduction to machine learning is presented that
includes brief discussions of applications such as image
recognition, speech recognition, medical diagnostics, and
statistical arbitrage. To enhance accessibility, two chapters on
relevant aspects of probability theory are provided. Subsequent
chapters feature coverage of topics such as the pattern recognition
problem, optimal Bayes decision rule, the nearest neighbor rule,
kernel rules, neural networks, support vector machines, and
boosting.
Appendices throughout the book explore the relationship between
the discussed material and related topics from mathematics,
philosophy, psychology, and statistics, drawing insightful
connections between problems in these areas and statistical
learning theory. All chapters conclude with a summary section, a
set of practice questions, and a reference sections that supplies
historical notes and additional resources for further study.
An Elementary Introduction to Statistical Learning Theory
is an excellent book for courses on statistical learning theory,
pattern recognition, and machine learning at the
upper-undergraduate and graduate levels. It also serves as an
introductory reference for researchers and practitioners in the
fields of engineering, computer science, philosophy, and cognitive
science that would like to further their knowledge of the
topic.
O autorze
SANJEEV KULKARNI, Ph D, is Professor in the Department of
Electrical Engineering at Princeton University, where he is also an
affiliated faculty member in the Department of Operations Research
and Financial Engineering and the Department of Philosophy. Dr.
Kulkarni has published widely on statistical pattern recognition,
nonparametric estimation, machine learning, information theory, and
other areas. A Fellow of the IEEE, he was awarded Princeton
University’s President’s Award for Distinguished Teaching in
2007.
GILBERT HARMAN, Ph D, is James S. Mc Donnell Distinguished
University Professor in the Department of Philosophy at Princeton
University. A Fellow of the Cognitive Science Society, he is the
author of more than fifty published articles in his areas of
research interest, which include ethics, statistical learning
theory, psychology of reasoning, and logic.
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