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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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Paperback. Condition: Brand New. 120 pages. 7.50x5.10x0.50 inches. In Stock.
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Financial Signal Processing and Machine Learning
Language: English
Published by Wiley & Sons, Incorporated, John, 2016
ISBN 10: 1118745671 ISBN 13: 9781118745670
Condition: Very Good. Used book that is in excellent condition. May show signs of wear or have minor defects.
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Condition: 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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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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Reliable Reasoning
Seller: Rarewaves.com USA, London, LONDO, United Kingdom
Seller rating 5 out of 5 stars
Paperback. Condition: New.
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Condition: good. Fast Free Shipping â" Good condition book with a firm cover and clean, readable pages. Shows normal use, including some light wear or limited notes highlighting, yet remains a dependable copy overall. Supplemental items like CDs or access codes may not be included.
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An Elementary Introduction to Statistical Learning Theory
Seller: Buchpark, Maidenhead, Berkshire, United Kingdom
Seller rating 4 out of 5 stars
Condition: Fair. Condition: Fair | Pages: 234 | Language: English | Product Type: Books.
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An Elementary Introduction to Statistical Learning Theory
Seller: Buchpark, Maidenhead, Berkshire, United Kingdom
Seller rating 4 out of 5 stars
Condition: Fine. Condition: Fine | Pages: 234 | Language: English | Product Type: Books.
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Financial Signal Processing and Machine Learning
Seller: PBShop.store UK, Fairford, GLOS, United Kingdom
Seller rating 5 out of 5 stars
HRD. Condition: New. New Book. Shipped from UK. Established seller since 2000.
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Condition: New.
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Paperback. Condition: New.
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Condition: As New. Unread book in perfect condition.
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Condition: New.
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Condition: New. pp. 232 Index.
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Condition: New. pp. 232.
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Financial Signal Processing and Machine Learning
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
Condition: New.
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Financial Signal Processing and Machine Learning
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
Condition: As New. Unread book in perfect condition.
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Financial Signal Processing and Machine Learning
Language: English
Published by John Wiley & Sons Inc, 2016
ISBN 10: 1118745671 ISBN 13: 9781118745670
Seller: THE SAINT BOOKSTORE, Southport, United Kingdom
Seller rating 5 out of 5 stars
£ 93.09
£ 17.17 shipping
Ships from United Kingdom to U.S.A.Quantity: Over 20 available
Add to basketHardback. Condition: New. New copy - Usually dispatched within 4 working days. 642.
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Condition: New. pp. 448.
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An Elementary Introduction to Statistical Learning Theory (Hardcover)
Language: English
Published by John Wiley & Sons Inc, New York, 2011
ISBN 10: 0470641835 ISBN 13: 9780470641835
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.
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Condition: New. pp. 232.
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Elementary Introduction to Statistical Learning Theory
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
Condition: New.
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Condition: New. pp. 448.
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An Elementary Introduction to Statistical Learning Theory
Language: English
Published by John Wiley & Sons Inc, 2011
ISBN 10: 0470641835 ISBN 13: 9780470641835
Seller: THE SAINT BOOKSTORE, Southport, United Kingdom
Seller rating 5 out of 5 stars
£ 107.07
£ 16.05 shipping
Ships from United Kingdom to U.S.A.Quantity: Over 20 available
Add to basketHardback. Condition: New. New copy - Usually dispatched within 4 working days. 530.
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Financial Signal Processing and Machine Learning (Hardcover)
Language: English
Published by John Wiley & Sons Inc, New York, 2016
ISBN 10: 1118745671 ISBN 13: 9781118745670
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 our UK warehouse or from our Australian or US warehouses, depending on stock availability.
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Fundamentals of Financial Signal Processing and Machine Learning
Language: English
Published by IEEE COMPUTER SOC PR, 2016
ISBN 10: 1118745671 ISBN 13: 9781118745670
Condition: New. 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.KlappentextThe modern financial industry has been required to deal with .
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Elementary Introduction to Statistical Learning Theory
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
£ 121.99
£ 15 shipping
Ships from United Kingdom to U.S.A.Quantity: Over 20 available
Add to basketCondition: As New. Unread book in perfect condition.
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Hardcover. Condition: Brand New. 1st edition. 320 pages. 9.75x7.00x1.00 inches. In Stock.
