Sprache: Englisch
Verlag: LAP LAMBERT Academic Publishing, 2020
ISBN 10: 6200785260 ISBN 13: 9786200785268
Anbieter: preigu, Osnabrück, Deutschland
Taschenbuch. Zustand: Neu. Document Classification Algorithms | And Feature Selection Techniques with the Practical Test | Esraa Hussein (u. a.) | Taschenbuch | 128 S. | Englisch | 2020 | LAP LAMBERT Academic Publishing | EAN 9786200785268 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu.
Sprache: Englisch
Verlag: LAP LAMBERT Academic Publishing Apr 2020, 2020
ISBN 10: 6200785260 ISBN 13: 9786200785268
Anbieter: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Deutschland
Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Documents classification is one of the most important fields in Natural language processing and text mining. There are many algorithms can be used to perform this task. Most of the used algorithms are from machine learning like: Decision Tree, Support Vector Machine, K-Nearest Neighbors and Naïve Bayes. These are the most essential four classification algorithms. Many researches try to modify and improve these algorithms for text classification. In this book, our work is divided into two levels: (i) a comparative study for these four algorithms, (ii) studying the improvement of document classification with feature selection where four feature selection methods are used and a new feature selection method is suggested. 128 pp. Englisch.
Sprache: Englisch
Verlag: LAP LAMBERT Academic Publishing, 2020
ISBN 10: 6200785260 ISBN 13: 9786200785268
Anbieter: moluna, Greven, Deutschland
EUR 45,45
Anzahl: Mehr als 20 verfügbar
In den WarenkorbZustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Hussein EsraaClassification is one of the most widely used techniques in machine learning. It can be standalone application as in Text Classification or a part from other field as in data mining and text mining. It is the process of .
Sprache: Englisch
Verlag: LAP LAMBERT Academic Publishing Apr 2020, 2020
ISBN 10: 6200785260 ISBN 13: 9786200785268
Anbieter: buchversandmimpf2000, Emtmannsberg, BAYE, Deutschland
Taschenbuch. Zustand: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Documents classification is one of the most important fields in Natural language processing and text mining. There are many algorithms can be used to perform this task. Most of the used algorithms are from machine learning like: Decision Tree, Support Vector Machine, K-Nearest Neighbors and Naïve Bayes. These are the most essential four classification algorithms. Many researches try to modify and improve these algorithms for text classification. In this book, our work is divided into two levels: (i) a comparative study for these four algorithms, (ii) studying the improvement of document classification with feature selection where four feature selection methods are used and a new feature selection method is suggested.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 128 pp. Englisch.
Sprache: Englisch
Verlag: LAP LAMBERT Academic Publishing, 2020
ISBN 10: 6200785260 ISBN 13: 9786200785268
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Documents classification is one of the most important fields in Natural language processing and text mining. There are many algorithms can be used to perform this task. Most of the used algorithms are from machine learning like: Decision Tree, Support Vector Machine, K-Nearest Neighbors and Naïve Bayes. These are the most essential four classification algorithms. Many researches try to modify and improve these algorithms for text classification. In this book, our work is divided into two levels: (i) a comparative study for these four algorithms, (ii) studying the improvement of document classification with feature selection where four feature selection methods are used and a new feature selection method is suggested.