{"product_id":"an-elementary-introduction-to-statistical-sanjeev-kulkarni-9780470641835","title":"An Elementary Introduction to Statistical Learning Theory","description":"\u003cb\u003eA thought-provoking look at statistical learning theory and its role in understanding human learning and inductive reasoning\u003c\/b\u003e\u003cbr\u003e \u003cbr\u003e \u003cp\u003eA joint endeavor from leading researchers in the fields of philosophy and electrical engineering, \u003ci\u003eAn Elementary Introduction to Statistical Learning Theory\u003c\/i\u003e 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.\u003c\/p\u003e \u003cp\u003ePromoting 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.\u003c\/p\u003e \u003cp\u003eAppendices 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.\u003c\/p\u003e \u003cp\u003e\u003ci\u003eAn Elementary Introduction to Statistical Learning Theory\u003c\/i\u003e 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.\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAuthor:\u003c\/b\u003e Sanjeev Kulkarni, Gilbert Harman\u003cbr\u003e\u003cb\u003eISBN-10:\u003c\/b\u003e 0470641835\u003cbr\u003e\u003cb\u003eISBN-13:\u003c\/b\u003e 9780470641835\u003cbr\u003e\u003cb\u003ePublisher:\u003c\/b\u003e Wiley\u003cbr\u003e\u003cb\u003eLanguage:\u003c\/b\u003e English\u003cbr\u003e\u003cb\u003ePublished:\u003c\/b\u003e 08\/02\/2011\u003cbr\u003e\u003cb\u003ePages:\u003c\/b\u003e 232\u003cbr\u003e\u003cb\u003eFormat:\u003c\/b\u003e Hardcover\u003cbr\u003e\u003cb\u003eWeight:\u003c\/b\u003e 1.10lbs\u003cbr\u003e\u003cb\u003eSize:\u003c\/b\u003e 9.30h x 6.10w x 0.70d\u003cbr\u003e\u003cbr\u003e\u003cb\u003eReview Citation(s): \u003c\/b\u003e\u003cbr\u003e\u003ci\u003eReference and Research Bk News\u003c\/i\u003e 10\/01\/2011 pg. 204","brand":"Sanjeev Kulkarni","offers":[{"title":"Hardcover","offer_id":44056171544831,"sku":"9780470641835","price":130.95,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0662\/2982\/9887\/files\/img_17b21010-28ff-4373-baab-684437f41ca0.jpg?v=1685039897","url":"https:\/\/www.whiterainbookhouse.com\/products\/an-elementary-introduction-to-statistical-sanjeev-kulkarni-9780470641835","provider":"WR Book House","version":"1.0","type":"link"}