[EAN: 9783639418187], Neubuch, [PU: AV Akademikerverlag], nach der Bestellung gedruckt Neuware -Revision with unchanged content. In many predictive modeling tasks, one has a fixed set of … Mehr…
[EAN: 9783639418187], Neubuch, [PU: AV Akademikerverlag], nach der Bestellung gedruckt 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, Books<
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 … Mehr…
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. Bücher / Sozialwissenschaften, Recht & Wirtschaft / Sozialwissenschaften allgemein, [PU: VDM Verlag Dr. Müller, Saarbrücken]<
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 … Mehr…
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. Bücher, Hörbücher & Kalender / Bücher / Sachbuch / Pädagogik / Sozialarbeit, [PU: VDM Verlag Dr. Müller, Saarbrücken]<
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[EAN: 9783639418187], Neubuch, [PU: AV Akademikerverlag], nach der Bestellung gedruckt Neuware -Revision with unchanged content. In many predictive modeling tasks, one has a fixed set of … Mehr…
[EAN: 9783639418187], Neubuch, [PU: AV Akademikerverlag], nach der Bestellung gedruckt 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, Books<
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 … Mehr…
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. Bücher / Sozialwissenschaften, Recht & Wirtschaft / Sozialwissenschaften allgemein, [PU: VDM Verlag Dr. Müller, Saarbrücken]<
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 … Mehr…
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. Bücher, Hörbücher & Kalender / Bücher / Sachbuch / Pädagogik / Sozialarbeit, [PU: VDM Verlag Dr. Müller, Saarbrücken]<
Nr. 97Q587S35GQ. Versandkosten:, Lieferzeit: 5 Tage, DE. (EUR 0.00)
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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.
Detailangaben zum Buch - Feature Selection in Data Mining
EAN (ISBN-13): 9783639418187 ISBN (ISBN-10): 3639418182 Gebundene Ausgabe Taschenbuch Erscheinungsjahr: 2012 Herausgeber: AV Akademikerverlag
Buch in der Datenbank seit 2009-11-28T22:46:35+01:00 (Berlin) Detailseite zuletzt geändert am 2023-03-29T23:08:03+02:00 (Berlin) ISBN/EAN: 9783639418187
ISBN - alternative Schreibweisen: 3-639-41818-2, 978-3-639-41818-7 Alternative Schreibweisen und verwandte Suchbegriffe: Titel des Buches: best selection, data mining
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