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Introduction to machine learning /
~
Alpaydin, Ethem.
Introduction to machine learning /
紀錄類型:
書目-語言資料,印刷品 : Monograph/item
杜威分類號:
006.3/1
書名/作者:
Introduction to machine learning // Ethem Alpaydin.
作者:
Alpaydin, Ethem.
出版者:
Cambridge, Mass. : : MIT Press,, c2014.
面頁冊數:
xxii, 613 p. : : ill. ;; 24 cm.
標題:
Machine learning.
ISBN:
9780262028189 (hbk.) :
書目註:
Includes bibliographical references and index.
內容註:
Introduction -- Supervised learning -- Bayesian decision theory -- Parametric methods -- Multivariate methods -- Dimensionality reduction -- Clustering -- Nonparametric methods -- Decision trees -- Linear discrimination -- Multilayer perceptrons -- Local models -- Kernel machines -- Graphical models -- Hidden markov models -- Bayesian estimation -- Combining multiple learners -- Reinforcement learning -- Design and analysis of machine learning experiments.
Introduction to machine learning /
Alpaydin, Ethem.
Introduction to machine learning /
Ethem Alpaydin. - 3rd ed. - Cambridge, Mass. :MIT Press,c2014. - xxii, 613 p. :ill. ;24 cm. - Adaptive computation and machine learning.
Includes bibliographical references and index.
Introduction -- Supervised learning -- Bayesian decision theory -- Parametric methods -- Multivariate methods -- Dimensionality reduction -- Clustering -- Nonparametric methods -- Decision trees -- Linear discrimination -- Multilayer perceptrons -- Local models -- Kernel machines -- Graphical models -- Hidden markov models -- Bayesian estimation -- Combining multiple learners -- Reinforcement learning -- Design and analysis of machine learning experiments.
ISBN: 9780262028189 (hbk.) :NTD 2,015
LCCN: 2014007214Subjects--Topical Terms:
202931
Machine learning.
LC Class. No.: Q325.5 / .A46 2014
Dewey Class. No.: 006.3/1
Introduction to machine learning /
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Introduction -- Supervised learning -- Bayesian decision theory -- Parametric methods -- Multivariate methods -- Dimensionality reduction -- Clustering -- Nonparametric methods -- Decision trees -- Linear discrimination -- Multilayer perceptrons -- Local models -- Kernel machines -- Graphical models -- Hidden markov models -- Bayesian estimation -- Combining multiple learners -- Reinforcement learning -- Design and analysis of machine learning experiments.
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