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Kleinster Preis: 38.99 EUR, größter Preis: 43.64 EUR, Mittelwert: 41.54 EUR
Feature Selection in Data Mining - Zhou, Jing
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Zhou, Jing:

Feature Selection in Data Mining - Taschenbuch

2007, ISBN: 3836427117, Lieferbar binnen 4-6 Wochen Versandkosten:Versandkostenfrei innerhalb der BRD

ID: 9783836427111

Internationaler Buchtitel. In englischer Sprache. Verlag: VDM Verlag, Paperback, 104 Seiten, L=240mm, B=170mm, H=6mm, Gew.=212gr, [GR: 16390 - HC/Informatik/EDV/Sonstiges], Kartoniert/Broschiert, Klappentext: 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. 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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Feature Selection in Data Mining - Approaches Based on Information Theory - Jing Zhou
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Jing Zhou:

Feature Selection in Data Mining - Approaches Based on Information Theory - neues Buch

ISBN: 9783836427111

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. Textbooks New Books ~~ Computers~~ Data Processing Feature-Selection-in-Data-Mining-Approaches-Based-on-Information-Theory~~Jing-Zhou VDM Verlag 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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Feature Selection in Data Mining - Approaches Based on Information Theory - Zhou, Jing
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Zhou, Jing:
Feature Selection in Data Mining - Approaches Based on Information Theory - gebrauchtes Buch

ISBN: 9783836427111

ID: 8052897

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. Feature Selection in Data Mining - Approaches Based on Information Theory Zhou, Jing, VDM Verlag

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Details zum Buch
Feature Selection in Data Mining
Autor:

Zhou, Jing

Titel:

Feature Selection in Data Mining

ISBN-Nummer:

9783836427111

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 20.02.2008 20:52:33
Buch zuletzt gefunden am 23.10.2016 16:59:09
ISBN/EAN: 9783836427111

ISBN - alternative Schreibweisen:
3-8364-2711-7, 978-3-8364-2711-1

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