Paperback, [PU: VDM Verlag Dr. Mueller E.K.], In many predictive modeling tasks, one has a fixed set of observations from which a vast, or even infinite, set of potentially predictive fea… Mehr…
Paperback, [PU: VDM Verlag Dr. Mueller E.K.], 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., Production Engineering, Computer Science<
Paperback, [PU: VDM Verlag Dr. Mueller E.K.], In many predictive modeling tasks, one has a fixed set of observations from which a vast, or even infinite, set of potentially predictive fea… Mehr…
Paperback, [PU: VDM Verlag Dr. Mueller E.K.], 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., Production Engineering<
Paperback, [PU: VDM Verlag Dr. Mueller e.K.], In many predictive modeling tasks, one has a fixed set of observations from which a vast, or even infinite, set of potentially predictive fea… Mehr…
Paperback, [PU: VDM Verlag Dr. Mueller e.K.], 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., Production Engineering<
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, ofte… Mehr…
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. Bücher > Fremdsprachige Bücher > Englische Bücher 24.0 cm x 17.0 cm x 0.6 cm mm , VDM, Taschenbuch, VDM<
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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, ofte… Mehr…
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. Buch 24.0 x 17.0 x 0.6 cm , VDM, VDM<
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(*) Derzeit vergriffen bedeutet, dass dieser Titel momentan auf keiner der angeschlossenen Plattform verfügbar ist.
Paperback, [PU: VDM Verlag Dr. Mueller E.K.], In many predictive modeling tasks, one has a fixed set of observations from which a vast, or even infinite, set of potentially predictive fea… Mehr…
Paperback, [PU: VDM Verlag Dr. Mueller E.K.], 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., Production Engineering, Computer Science<
Paperback, [PU: VDM Verlag Dr. Mueller E.K.], In many predictive modeling tasks, one has a fixed set of observations from which a vast, or even infinite, set of potentially predictive fea… Mehr…
Paperback, [PU: VDM Verlag Dr. Mueller E.K.], 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., Production Engineering<
Paperback, [PU: VDM Verlag Dr. Mueller e.K.], In many predictive modeling tasks, one has a fixed set of observations from which a vast, or even infinite, set of potentially predictive fea… Mehr…
Paperback, [PU: VDM Verlag Dr. Mueller e.K.], 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., Production Engineering<
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, ofte… Mehr…
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. Bücher > Fremdsprachige Bücher > Englische Bücher 24.0 cm x 17.0 cm x 0.6 cm mm , VDM, Taschenbuch, VDM<
Nr. A1001600685. Versandkosten:Lieferzeiten außerhalb der Schweiz 3 bis 21 Werktage, , in stock, zzgl. Versandkosten. (EUR 18.22)
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, ofte… Mehr…
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. Buch 24.0 x 17.0 x 0.6 cm , VDM, VDM<
Nr. A1001600685. Versandkosten:, , zzgl. Versandkosten. (EUR 8.00)
1Da einige Plattformen keine Versandkonditionen übermitteln und diese vom Lieferland, dem Einkaufspreis, dem Gewicht und der Größe des Artikels, einer möglichen Mitgliedschaft der Plattform, einer direkten Lieferung durch die Plattform oder über einen Drittanbieter (Marketplace), etc. abhängig sein können, ist es möglich, dass die von eurobuch angegebenen Versandkosten nicht mit denen der anbietenden Plattform übereinstimmen.
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): 9783836427111 ISBN (ISBN-10): 3836427117 Taschenbuch Erscheinungsjahr: 2007 Herausgeber: VDM Verlag 104 Seiten Gewicht: 0,212 kg Sprache: eng/Englisch
Buch in der Datenbank seit 2008-02-20T20:52:33+01:00 (Berlin) Detailseite zuletzt geändert am 2024-03-09T00:53:53+01:00 (Berlin) ISBN/EAN: 9783836427111
ISBN - alternative Schreibweisen: 3-8364-2711-7, 978-3-8364-2711-1 Alternative Schreibweisen und verwandte Suchbegriffe: Autor des Buches: zhou, jing Titel des Buches: data mining, selection
Daten vom Verlag:
Autor/in: Jing Zhou Titel: Feature Selection in Data Mining - Approaches Based on Information Theory Verlag: VDM Verlag Dr. Müller 104 Seiten Erscheinungsjahr: 2007-09-23 Sprache: Englisch 42,00 € (DE) 43,20 € (AT) 70,00 CHF (CH) Not available (reason unspecified)
BC; PB; Hardcover, Softcover / Informatik, EDV; Informatik und Informationstechnologie; Sozialwissenschaften, Soziologie; discovery rate; regression; algorithms; selection; classification; data mining
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