Synopsis
Revision with unchanged content. In many predictive modeling tasks, one has a fixed set of observations from which a vast, or even infinite, set of potentially predictive features can be computed. Of these features, often only a small number are expected to be useful in a predictive model. Models which use the entire set of features will almost certainly overfit on future data sets. The book presents streamwise feature selection which interleaves the process of generating new features with that of feature testing. Streamwise feature selection scales well to large feature sets. The book also describes how to use streamwise feature seleciton in multivariate regressions. It includes a review of traditional feature selecitions in a general framework based on information theory, and compares these methods with streamwise feature selection on various real and synthetic data sets. This book is intended to be used by researchers in machine learning, data mining, and knowledge discovery.
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About the Author
Is an Applied Science Researcher in Microsoft, solving research problems that impact business performance and building advanced prototypes. He received a Ph.D. from the University of Pennsylvania.
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Feature Selection in Data Mining
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AV Akademikerverlag Mai 2012, 2012
ISBN 10: 3639418182
ISBN 13: 9783639418187
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Revision with unchanged content. In many predictive modeling tasks, one has a fixed set of observations from which a vast, or even infinite, set of potentially predictive features can be com puted. Of these features, often only a small number are expected to be use ful in a predictive model. Models which use the entire set of features will almost certainly overfit on future data sets. The book presents streamwise feature selection which interleaves the pro cess of generating new features with that of feature testing. Streamwise fea ture selection scales well to large feature sets. The book also describes how to use streamwise feature seleciton in multivariate regressions. It includes a review of traditional feature selecitions in a general frame work based on information theory, and compares these methods with streamwise feature selection on various real and synthetic data sets. This book is intended to be used by researchers in machine learning, data mining, and knowledge discovery. 104 pp. Englisch. Seller Inventory # 9783639418187
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Feature Selection in Data Mining: Approaches Based on.
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Feature Selection in Data Mining
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Zhou JingIs an Applied Science Researcher in Microsoft, solving research problems that impact business performance and building advanced prototypes. He received a Ph.D. from the University of Pennsylvania.Revision with unchanged . Seller Inventory # 4986033
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Feature Selection in Data Mining
Published by
AV Akademikerverlag Mai 2012, 2012
ISBN 10: 3639418182
ISBN 13: 9783639418187
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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Revision with unchanged content. In many predictive modeling tasks, one has a fixed set of observations from which a vast, or even infinite, set of potentially predictive features can be computed. Of these features, often only a small number are expected to be useful in a predictive model. Models which use the entire set of features will almost certainly overfit on future data sets. The book presents streamwise feature selection which interleaves the process of generating new features with that of feature testing. Streamwise feature selection scales well to large feature sets. The book also describes how to use streamwise feature seleciton in multivariate regressions. It includes a review of traditional feature selecitions in a general framework based on information theory, and compares these methods with streamwise feature selection on various real and synthetic data sets. This book is intended to be used by researchers in machine learning, data mining, and knowledge discovery.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 104 pp. Englisch. Seller Inventory # 9783639418187
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Feature Selection in Data Mining : Approaches Based on Information Theory
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Revision with unchanged content. In many predictive modeling tasks, one has a fixed set of observations from which a vast, or even infinite, set of potentially predictive features can be com puted. Of these features, often only a small number are expected to be use ful in a predictive model. Models which use the entire set of features will almost certainly overfit on future data sets. The book presents streamwise feature selection which interleaves the pro cess of generating new features with that of feature testing. Streamwise fea ture selection scales well to large feature sets. The book also describes how to use streamwise feature seleciton in multivariate regressions. It includes a review of traditional feature selecitions in a general frame work based on information theory, and compares these methods with streamwise feature selection on various real and synthetic data sets. This book is intended to be used by researchers in machine learning, data mining, and knowledge discovery. Seller Inventory # 9783639418187
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Feature Selection in Data Mining | Approaches Based on Information Theory
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Taschenbuch. Condition: Neu. Feature Selection in Data Mining | Approaches Based on Information Theory | Jing Zhou | Taschenbuch | 104 S. | Englisch | 2012 | AV Akademikerverlag | EAN 9783639418187 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu. Seller Inventory # 106435953
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Feature Selection in Data Mining: Approaches Based on Information Theory
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