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Condition: Very Good. 1st Edition. Used book that is in excellent condition. May show signs of wear or have minor defects.
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Hardcover. Condition: Very Good. No Jacket. Former library book; May have limited writing in cover pages. Pages are unmarked. ~ ThriftBooks: Read More, Spend Less 0.6.
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An Elementary Introduction to Statistical Learning Theory: 853 (Wiley Series in Probability and Statistics)
Published by John Wiley & Sons Inc, United States, New York, 2011
ISBN 10: 0470641835 ISBN 13: 9780470641835
Language: English
Seller: WorldofBooks, Goring-By-Sea, WS, United Kingdom
Seller rating 5 out of 5 stars
£ 22.14
Convert currency£ 5.60 shipping from United Kingdom to U.S.A.Quantity: 3 available
Add to basketPaperback. Condition: Very Good. 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 graduatelevels. 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. The book has been read, but is in excellent condition. Pages are intact and not marred by notes or highlighting. The spine remains undamaged.
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Reliable Reasoning
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Add to basketPaperback. Condition: New.
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Reliable Reasoning: Induction and Statistical Learning Theory (Jean Nicod Lectures)
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Add to basketPaperback. Condition: Brand New. 120 pages. 7.50x5.10x0.50 inches. In Stock.
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Condition: very_good. Book is in very good condition and may include minimal underlining highlighting. The book can also include "From the library of" labels. May not contain miscellaneous items toys, dvds, etc. . We offer 100% money back guarantee and 24 7 customer service.
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£ 33.15
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Add to basketCondition: New. Gilbert Harman is Stuart Professor of Philosophy at Princeton University and the author of Explaining Value and Other Essays in Moral Philosophy and Reasoning, Meaning, and Mind.Sanjeev Kulkarni is Professor of Electrical Engineering a.
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Financial Signal Processing and Machine Learning
Published by Wiley & Sons, Incorporated, John, 2016
ISBN 10: 1118745671 ISBN 13: 9781118745670
Language: English
Condition: Very Good. Used book that is in excellent condition. May show signs of wear or have minor defects.
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An Elementary Introduction to Statistical Learning Theory
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Add to basketCondition: Fair. Condition: Fair | Pages: 234 | Language: English | Product Type: Books.
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An Elementary Introduction to Statistical Learning Theory
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Add to basketCondition: Fine. Condition: Fine | Pages: 234 | Language: English | Product Type: Books.
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Financial Signal Processing and Machine Learning
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Add to basketHRD. Condition: New. New Book. Shipped from UK. Established seller since 2000.
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Financial Signal Processing and Machine Learning (Hardcover)
Published by John Wiley & Sons Inc, New York, 2016
ISBN 10: 1118745671 ISBN 13: 9781118745670
Language: English
First Edition
Hardcover. Condition: new. Hardcover. The modern financial industry has been required to deal with large and diverse portfolios in a variety of asset classes often with limited market data available. Financial Signal Processing and Machine Learning unifies a number of recent advances made in signal processing and machine learning for the design and management of investment portfolios and financial engineering. This book bridges the gap between these disciplines, offering the latest information on key topics including characterizing statistical dependence and correlation in high dimensions, constructing effective and robust risk measures, and their use in portfolio optimization and rebalancing. The book focuses on signal processing approaches to model return, momentum, and mean reversion, addressing theoretical and implementation aspects. It highlights the connections between portfolio theory, sparse learning and compressed sensing, sparse eigen-portfolios, robust optimization, non-Gaussian data-driven risk measures, graphical models, causal analysis through temporal-causal modeling, and large-scale copula-based approaches. Key features: Highlights signal processing and machine learning as key approaches to quantitative finance.Offers advanced mathematical tools for high-dimensional portfolio construction, monitoring, and post-trade analysis problems.Presents portfolio theory, sparse learning and compressed sensing, sparsity methods for investment portfolios. including eigen-portfolios, model return, momentum, mean reversion and non-Gaussian data-driven risk measures with real-world applications of these techniques.Includes contributions from leading researchers and practitioners in both the signal and information processing communities, and the quantitative finance community. The modern financial industry has been required to deal with large and diverse portfolios in a variety of asset classes often with limited market data available. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Financial Signal Processing and Machine Learning (IEEE Press)
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Financial Signal Processing and Machine Learning
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Add to basketCondition: New. pp. 232.
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Condition: New. pp. 232 Index.
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Financial Signal Processing and Machine Learning
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An Elementary Introduction to Statistical Learning Theory
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Financial Signal Processing and Machine Learning
Published by John Wiley & Sons Inc, 2016
ISBN 10: 1118745671 ISBN 13: 9781118745670
Language: English
Seller: THE SAINT BOOKSTORE, Southport, United Kingdom
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£ 93.09
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Add to basketHardback. Condition: New. New copy - Usually dispatched within 4 working days. 642.
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Add to basketCondition: New. pp. 448.
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Elementary Introduction to Statistical Learning Theory
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An Elementary Introduction to Statistical Learning Theory (Hardcover)
Published by John Wiley & Sons Inc, New York, 2011
ISBN 10: 0470641835 ISBN 13: 9780470641835
Language: English
First Edition
Hardcover. Condition: new. Hardcover. 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. * Serves as a fundamental introduction to statistical learning theory and its role in understanding human learning and inductive reasoning. * Topics of coverage include: probability, pattern recognition, optimal Bayes decision rule, nearest neighbor rule, kernel rules, neural networks, and support vector machines. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
