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An Elementary Introduction to Statistical Learning Theory (Kulkarni/Statistical Learning Theory) by Kulkarni, Sanjeev., Harman, Gilbert. is a scholarly article available to read on EtoBox.

What is An Elementary Introduction to Statistical Learning Theory (Kulkarni/Statistical Learning Theory) about?

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 r

Author

Kulkarni, Sanjeev., Harman, Gilbert.

Publisher

John Wiley & Sons, Incorporated

Published

2011

Language

EN

ISBN

9781118023464