2, ISBN: 9781139227285
ID: 101159781139227285
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 conditi 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 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. Computer Vision, Engineering, Density Ratio Estimation in Machine Learning~~ Sugiyama, Masashi~~Computer Vision~~Engineering~~9781139227285, en, Density Ratio Estimation in Machine Learning, Sugiyama, Masashi, 9781139227285, Cambridge University Press, 02/01/2012, , , , Cambridge University Press, 02/01/2012
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2012, ISBN: 9781139227285
ID: 16409544
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. 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 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. eBooks, , Density Ratio Estimation In Machine Learning~~EBook~~9781139227285~~Masashi Sugiyama, Taiji Suzuki, Takafumi Kanamori, , Density Ratio Estimation In Machine Learning, Masashi Sugiyama, 9781139227285, Cambridge University Press, 02/20/2012, , , , Cambridge University Press
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ISBN: 9781139227285
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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 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. Density Ratio Estimation in Machine Learning eBook eBooks>Fremdsprachige eBooks>Englische eBooks>Sach- & Fachthemen>Informatik, Cambridge University Press
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ISBN: 9781139227285
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 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. eBooks Density Ratio Estimation In Machine Learning~~EBook~~9781139227285~~Masashi Sugiyama, Taiji Suzuki, Takafumi Kanamori Density Ratio Estimation In Machine Learning
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2012, ISBN: 9781139227285
ID: 26268756
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Title: | Density Ratio Estimation in Machine Learning |
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Details of the book - Density Ratio Estimation in Machine Learning
EAN (ISBN-13): 9781139227285
Publishing year: 2012
Publisher: Cambridge University Press
Book in our database since 26.09.2007 12:13:20
Book found last time on 22.06.2016 21:08:31
ISBN/EAN: 9781139227285
ISBN - alternate spelling:
978-1-139-22728-5
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