Density Ratio Estimation in Machine Learning

Masashi Sugiyama, Taiji Suzuki, Takafumi Kanamori

  • 出版商: Cambridge
  • 出版日期: 2012-02-20
  • 售價: $5,730
  • 貴賓價: 9.5$5,444
  • 語言: 英文
  • 頁數: 342
  • 裝訂: Hardcover
  • ISBN: 0521190177
  • ISBN-13: 9780521190176
  • 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與工程領域,研究學習系統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,這是一個在機器學習社群中新興的範式。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,都可以通過概率密度比的估計系統性地解決。作者提供了各種密度比估計器的全面介紹,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的方法,並描述了這些方法如何應用於機器學習。本書還提供了密度比估計的數學理論,包括參數和非參數的收斂分析以及數值穩定性分析,完整呈現了機器學習中密度比估計的整體框架的首次和權威性處理。