kulkarni sanjeev (15 Ergebnisse)

Paperback. Zustand: Brand New. 120 pages. 7.50x5.10x0.50 inches. In Stock.

Zustand: Very Good. Used book that is in excellent condition. May show signs of wear or have minor defects.

Zustand: 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.

Zustand: Fair. Condition: Fair | Pages: 234 | Language: English | Product Type: Books.

Zustand: Fine. Condition: Fine | Pages: 234 | Language: English | Product Type: Books.

HRD. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

Zustand: New. pp. 232.

Zustand: New. pp. 448.

Zustand: 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 .

Hardcover. Zustand: Brand New. 1st edition. 320 pages. 9.75x7.00x1.00 inches. In Stock.

Zustand: 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. Editor(s): Akansu, Ali N.; Kulkarni, Sanjeev R.; Malioutov, Dmitry M.; Pollak, Ilya. Series: Wiley - IEEE. Num Pages: 320 pages, illustrations. BIC Classification: TJK; UYQM; UYS. Category: (P) Professional & Vocational. Dimension: 178 x 251 x 19. Weight in Grams: 626. . 2016. 1st Edition. Hardcover. . . . . Books ship from the US and Ireland.

Hardcover. Zustand: Brand New. 1st edition. 232 pages. 9.50x6.25x0.50 inches. In Stock.

Zustand: New. * 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. Series: Wiley Series in Probability and Statistics. Num Pages: 232 pages, Illustrations. BIC Classification: PBT; UYQM. Category: (P) Professional & Vocational. Dimension: 237 x 162 x 17. Weight in Grams: 496. . 2011. 1st Edition. Hardcover. . . . . Books ship from the US and Ireland.

Buch. Zustand: Neu. Neuware - 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.

Buch. Zustand: Neu. Neuware - A thought-provoking look at statistical learning theory and its role in understanding human learning and inductive reasoningA 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.