Автор: Trevor Hastie; Robert Tibshirani; Jerome Friedman Название: The Elements of Statistical Learning ISBN: 0387848576 ISBN-13(EAN): 9780387848570 Издательство: Springer Рейтинг: Цена: 10480.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This major new edition features many topics not covered in the original, including graphical models, random forests, and ensemble methods. As before, it covers the conceptual framework for statistical data in our rapidly expanding computerized world.
Автор: Christopher M. Bishop Название: Pattern Recognition and Machine Learning ISBN: 0387310738 ISBN-13(EAN): 9780387310732 Издательство: Springer Рейтинг: Цена: 11878.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.
Автор: Stuber, Gordon L. Название: Principles of Mobile Communication ISBN: 1461403634 ISBN-13(EAN): 9781461403630 Издательство: Springer Рейтинг: Цена: 15672.00 р. Наличие на складе: Поставка под заказ.
Описание: Principles of Mobile Communication, Third Edition, is an authoritative treatment of the fundamentals of mobile communications. This book stresses the fundamentals of physical-layer wireless and mobile communications engineering that are important for the design of any wireless system. This book differs from others in the field by stressing mathematical modeling and analysis. It includes many detailed derivations from first principles, extensive literature references, and provides a level of depth that is necessary for graduate students wishing to pursue research on this topic. The book's focus will benefit students taking formal instruction and practicing engineers who are likely to already have familiarity with the standards and are seeking to increase their knowledge of this important subject. Major changes from the second edition: 1. Updated discussion of wireless standards (Chapter 1). 2. Updated treatment of land mobile radio propagation to include space-time correlation functions, mobile-to-mobile (or vehicle-to-vehicle) channels, multiple-input multiple-output (MIMO) channels, improved simulation models for land mobile radio channels, and 3G cellular simulation models. 3. Updated treatment of modulation techniques and power spectrum to include Nyquist pulse shaping and linearized Gaussian minimum shift keying (LGMSK). 4. Updated treatment of antenna diversity techniques to include optimum combining, non-coherent square-law combining, and classical beamforming. 5. Updated treatment of error control coding to include space-time block codes, the BCJR algorithm, bit interleaved coded modulation, and space-time trellis codes. 6. Updated treatment of spread spectrum to include code division multiple access (CDMA) multi-user detection techniques. 7. A completely new chapter on multi-carrier techniques to include the performance of orthogonal frequency division multiplexing (OFDM) on intersymbol interference (ISI) channels, OFDM residual ISI cancellation, single-carrier frequency domain equalization (SC-FDE), orthogonal frequency division multiple access (OFDMA) and single-carrier frequency division multiple access (SC-FDMA). 8. Updated discussion of frequency planning to include OFDMA frequency planning. 9. Updated treatment of CDMA cellular systems to include hierarchical CDMA cellular architectures and capacity analysis. 10. Updated treatment of radio resource management to include CDMA soft handoff analysis. Includes numerous homework problems throughout.
Автор: Malley Название: Statistical Learning for Biomedical Data ISBN: 0521699096 ISBN-13(EAN): 9780521699099 Издательство: Cambridge Academ Рейтинг: Цена: 6494.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Biomedical researchers need machine learning techniques to make predictions such as survival/death or response to treatment when data sets are large and complex. This highly motivating introduction to these machines explains underlying principles in nontechnical language, using many examples and figures, and connects these new methods to familiar techniques.
A comprehensive introduction to the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications.
Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications including price prediction, risk assessment, predicting customer behavior, and document classification. This introductory textbook offers a detailed and focused treatment of the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications. Technical and mathematical material is augmented with explanatory worked examples, and case studies illustrate the application of these models in the broader business context.
After discussing the trajectory from data to insight to decision, the book describes four approaches to machine learning: information-based learning, similarity-based learning, probability-based learning, and error-based learning. Each of these approaches is introduced by a nontechnical explanation of the underlying concept, followed by mathematical models and algorithms illustrated by detailed worked examples. Finally, the book considers techniques for evaluating prediction models and offers two case studies that describe specific data analytics projects through each phase of development, from formulating the business problem to implementation of the analytics solution. The book, informed by the authors' many years of teaching machine learning, and working on predictive data analytics projects, is suitable for use by undergraduates in computer science, engineering, mathematics, or statistics; by graduate students in disciplines with applications for predictive data analytics; and as a reference for professionals.
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