Density Ratio Estimation in Machine Learning

Masashi Sugiyama, Taiji Suzuki, Takafumi Kanamori

  • 出版商: Cambridge
  • 出版日期: 2018-03-29
  • 售價: $1,970
  • 貴賓價: 9.5$1,872
  • 語言: 英文
  • 頁數: 341
  • 裝訂: Paperback
  • ISBN: 1108461735
  • ISBN-13: 9781108461733
  • 相關分類: Machine Learning
  • 海外代購書籍(需單獨結帳)

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商品描述

Machine learning is an interdisciplinary field of science and engineering that studies mathematical theories and practical applications of systems that learn. This book introduces theories, methods, and applications of density ratio estimation, which is a newly emerging paradigm in the machine learning community. Various machine learning problems such as non-stationarity adaptation, outlier detection, dimensionality reduction, independent component analysis, clustering, classification, and conditional density estimation can be systematically solved via the estimation of probability density ratios. The authors offer a comprehensive introduction of various density ratio estimators including methods via density estimation, moment matching, probabilistic classification, density fitting, and density ratio fitting as well as describing how these can be applied to machine learning. The book also provides mathematical theories for density ratio estimation including parametric and non-parametric convergence analysis and numerical stability analysis to complete the first and definitive treatment of the entire framework of density ratio estimation in machine learning.

商品描述(中文翻譯)

機器學習是一個跨學科的科學和工程領域,研究系統學習的數學理論和實際應用。本書介紹了密度比估計的理論、方法和應用,這是機器學習社區中新興的範式。通過估計概率密度比,可以系統地解決各種機器學習問題,如非穩態適應、異常檢測、降維、獨立成分分析、聚類、分類和條件密度估計。作者們全面介紹了各種密度比估計方法,包括通過密度估計、矩匹配、概率分類、密度擬合和密度比擬合等方法,並描述了如何將這些方法應用於機器學習。本書還提供了密度比估計的數學理論,包括參數和非參數收斂分析以及數值穩定性分析,以完成對機器學習中密度比估計整個框架的首次全面且權威的介紹。