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# ðŸ“™ An Elementary Introduction to Statistical Learning Theory by Sanjeev Kulkarni, Gilbert Harman â€” download pdf

**A thought-provoking look at statistical learning theory and its role in understanding human learning and inductive reasoning**## About book:

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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 graduateÂ 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.

- Series:
**Wiley Series in Probability and Statistics** - Author:
**Sanjeev Kulkarni, Gilbert Harman** - Year:
**2011** - Publisher:
**Wiley** - Language:
**English** - ISBN:
**0470641835,9780470641835**

- File size:
**3 204 363** - Format:
**pdf**

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Statistical learning theory is aimed at analyzing complex data with necessarily approximate models. This book is intended for an audience with a graduate background in probability theory and statistics. It will be useful to any reader wondering why it may...

This volume consists of contributions by speakers at the AMS Special Session on Combinatorial and Statistical Group Theory held at New York University. Readers will find a variety of contributions, including survey papers on applications of group theory i...

This book constitutes the refereed proceedings of the 23rd International Conference on Algorithmic Learning Theory, ALT 2012, held in Lyon, France, in October 2012. The conference was co-located and held in parallel with the 15th International Conference ...

INTRODUCTION TO THE THEORY OF LINEAR DIFFERENTIAL EQUATIONS BY E. G. C. PODJLE Fellow of New College, Oxford OXFORD AT THE CLARENDON PRESS 193d Oui, 1 oeuvre sort plus belle Dune forme au travail Rebelle, Vers, marbre, onyx, email TH. GAUTIEB iZmaux et Ca...

Point processes and random measures find wide applicability in telecommunications, earthquakes, image analysis, spatial point patterns, and stereology, to name but a few areas. The authors have made a major reshaping of their work in their first edition o...

Information Theory and Statistical Learning presents theoretical and practical results about information theoretic methods used in the context of statistical learning. The book will present a comprehensive overview of the large range of different methods ...

This book constitutes the refereed proceedings of the 15th International Conference on Algorithmic Learning Theory, ALT 2004, held in Padova, Italy in October 2004.The 29 revised full papers presented together with 5 invited papers and 3 tutorial summarie...

This is the first book to collect essays from philosophers, mathematicians and computer scientists working at the exciting interface of algorithmic learning theory and the epistemology of science and inductive inference. Readable, introductory essays prov...

Algorithmic learning theory is mathematics about computer programs which learn from experience. This involves considerable interaction between various mathematical disciplines including theory of computation, statistics, and c- binatorics. There is also c...

In early 1968 the grisly on-the-job deaths of two African-American sanitation workers in Memphis, Tennessee, prompted an extended strike by that city's segregated force of trash collectors. Workers sought union protection, higher wages, improved safety, a...

A commemoration of brave yet largely forgotten women who served in the First World War In time for the 2014 centennial of the start of the Great War, this book brings to life the brave and often surprising exploits of 16 fascinating women from around the ...

"Music has been in my blood from the day I was born," said Louis Armstrong. Author Wendie C. Old weaves a rich, colorful tapestry of the life of this beloved and brilliant musician known to his fans as Satchmo. Louis Armstrong rose from poverty and a trou...