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Více informací o knize Principles and Theory for Data Mining and Machine Learning

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Anotace knihy

This book is a thorough introduction to the most important topics in data mining and machine learning. It begins with a detailed review of classical function estimation and proceeds with chapters on nonlinear regression, classification, and ensemble methods. The final chapters focus on clustering, dimension reduction, variable selection, and multiple comparisons. All these topics have undergone extraordinarily rapid development in recent years and this treatment offers a modern perspective emphasizing the most recent contributions. The presentation of foundational results is detailed and includes many accessible proofs not readily available outside original sources. While the orientation is conceptual and theoretical, the main points are regularly reinforced by computational comparisons.§Intended primarily as a graduate level textbook for statistics, computer science, and electrical engineering students, this book assumes only a strong foundation in undergraduate statistics and mathematics, and facility with using R packages. The text has a wide variety of problems, many of an exploratory nature. There are numerous computed examples, complete with code, so that further computations can be carried out readily. The book also serves as a handbook for researchers who want a conceptual overview of the central topics in data mining and machine learning.

Parametry knihy

Zařazení knihy Knihy v angličtině Computing & information technology Computer science Artificial intelligence

• Plný název: Principles and Theory for Data Mining and Machine Learning
• Autor: Bertrand Clarke
• Jazyk: Angličtina
• Vazba: Pevná
• Počet stran: 786
• EAN: 9780387981345
• ISBN: 0387981349
• ID: 01327947
• Nakladatelství: Springer-Verlag New York Inc.
• Hmotnost: 1284 g
• Rozměry: 241 × 156 × 59 mm
• Datum vydání: 30. July 2009

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