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: Paul Vitanyi Название: Computational Learning Theory ISBN: 3540591192 ISBN-13(EAN): 9783540591191 Издательство: Springer Рейтинг: Цена: 12157.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This volume of conference proceedings explores the computational aspects of artificial and natural learning systems and machine learning. Key issues discussed include neural networks, genetic algorithms, robotics, pattern recognition, decision theory and cryptography.
Автор: Paul Fischer; Hans U. Simon Название: Computational Learning Theory ISBN: 3540657010 ISBN-13(EAN): 9783540657019 Издательство: Springer Рейтинг: Цена: 9781.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This text presents the proceedings of the 4th European Conference on Computational Learning Theory. The 23 contributions address topics such as learning from queries and counter examples, reinforcement learning, online learning and export advice, teaching and learning and inductive inference.
Автор: Jyrki Kivinen; Robert H. Sloan Название: Computational Learning Theory ISBN: 354043836X ISBN-13(EAN): 9783540438366 Издательство: Springer Рейтинг: Цена: 12157.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Constitutes the proceedings of the 15th Annual Conference on Computational Learning Theory, held in Australia in 2002. The 26 papers cover statistical learning theory, online learning, inductive inference, PAC learning, boosting and other learning paradigms.
Автор: David Helmbold; Bob Williamson Название: Computational Learning Theory ISBN: 3540423435 ISBN-13(EAN): 9783540423430 Издательство: Springer Рейтинг: Цена: 14673.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This volume constitutes the refereed proceedings of the 14th Annual and 5th European Conferences on Computational Learning Theory, COLT/EuroCOLT 2001. All current aspects of computational learning and its applications in a variety of fields are addressed.
Автор: Clarke Название: Principles and Theory for Data Mining and Machine Learning ISBN: 0387981349 ISBN-13(EAN): 9780387981345 Издательство: Springer Рейтинг: Цена: 27950.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Extensive treatment of the most up-to-date topicsProvides the theory and concepts behind popular and emerging methodsRange of topics drawn from Statistics, Computer Science, and Electrical Engineering
Автор: Setsuo Arikawa; Arun K. Sharma Название: Algorithmic Learning Theory ISBN: 3540618635 ISBN-13(EAN): 9783540618638 Издательство: Springer Рейтинг: Цена: 11179.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Covering all areas related to algorithmic learning theory (ALT), ranging from theoretical foundations of machine learning to applications in several areas, this text presents papers from a workshop held on ALT in Sydney, in October 1996.
Автор: Hiroki Arimura; Sanjay Jain; Arun Sharma Название: Algorithmic Learning Theory ISBN: 3540412379 ISBN-13(EAN): 9783540412373 Издательство: Springer Рейтинг: Цена: 9781.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: These papers on algorithmic learning theory are organized in topical sections on statistical learning, inductive logic programming, inductive inference, complexity, neural networks and other paradigms, support vector machines.
Автор: Anthony Название: Computational Learning Theory ISBN: 0521599229 ISBN-13(EAN): 9780521599221 Издательство: Cambridge Academ Рейтинг: Цена: 6970.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This is a self contained volume in which the authors concentrate on the `probably approximately correct model`. It will therefore form an introduction to the theory of computational learning, suitable for a broad spectrum of graduate students from theoretical computer science and mathematics.
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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