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This book gives a classification algorithms like Support Vector Machine and Genetic Algorithm are used to find the classification accuracy for the Wisconsin Breast Cancer dataset. The benchmark dataset, Wisconsin Breast Cancer dataset is obtained from UCI Machine Learning Repository. The dataset consists of 699 instances divided into 2 classes namely Benign and Malignant, each with 11 attributes. Support vector machines (SVMs) are a set of related supervised learning methods used for classification. A classification SVM model attempts to separate the target classes with the widest possible margin. In SVM, Radial basis function and Polynomial kernel function are used to calculate classification accuracy and run time. Feature Selection is used to improve the accuracy of the SVM classifier.In GA, Integer and Binary Coded Genetic Algorithm are also used to calculate classification accuracy and run time. Integer- Coded Genetic Algorithm is used to select important and relevant features for classification. Binary Coded Genetic Algorithm can be applied to many optimization problems which contains binary string for the variables.
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Feature Selection using Genetic Algorithm to improve SVM Classifier
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LAP LAMBERT Academic Publishing Jan 2019, 2019
ISBN 10: 6139992079
ISBN 13: 9786139992072
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Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book gives a classification algorithms like Support Vector Machine and Genetic Algorithm are used to find the classification accuracy for the Wisconsin Breast Cancer dataset. The benchmark dataset, Wisconsin Breast Cancer dataset is obtained from UCI Machine Learning Repository. The dataset consists of 699 instances divided into 2 classes namely Benign and Malignant, each with 11 attributes. Support vector machines (SVMs) are a set of related supervised learning methods used for classification. A classification SVM model attempts to separate the target classes with the widest possible margin. In SVM, Radial basis function and Polynomial kernel function are used to calculate classification accuracy and run time. Feature Selection is used to improve the accuracy of the SVM classifier.In GA, Integer and Binary Coded Genetic Algorithm are also used to calculate classification accuracy and run time. Integer- Coded Genetic Algorithm is used to select important and relevant features for classification. Binary Coded Genetic Algorithm can be applied to many optimization problems which contains binary string for the variables. 104 pp. Englisch. Codice articolo 9786139992072
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Feature Selection using Genetic Algorithm to improve SVM Classifier
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ISBN 13: 9786139992072
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Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Devaraj NithyaD.Nithya received B.E degree CSE in 2008 and M.E degree CSE in 2010 from Avinashilingam University, Coimbatore. At present she is an Assistant Professor in Dept. of CSE, School of Engineering, Avinashlingam University,. Codice articolo 385662119
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Feature Selection using Genetic Algorithm to improve SVM Classifier
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LAP Lambert Academic Publishing, 2019
ISBN 10: 6139992079
ISBN 13: 9786139992072
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Paperback. Condizione: Brand New. 8.74x6.02x0.39 inches. In Stock. Codice articolo zk6139992079
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Feature Selection using Genetic Algorithm to improve SVM Classifier
Editore:
LAP LAMBERT Academic Publishing Jan 2019, 2019
ISBN 10: 6139992079
ISBN 13: 9786139992072
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Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book gives a classification algorithms like Support Vector Machine and Genetic Algorithm are used to find the classification accuracy for the Wisconsin Breast Cancer dataset. The benchmark dataset, Wisconsin Breast Cancer dataset is obtained from UCI Machine Learning Repository. The dataset consists of 699 instances divided into 2 classes namely Benign and Malignant, each with 11 attributes. Support vector machines (SVMs) are a set of related supervised learning methods used for classification. A classification SVM model attempts to separate the target classes with the widest possible margin. In SVM, Radial basis function and Polynomial kernel function are used to calculate classification accuracy and run time. Feature Selection is used to improve the accuracy of the SVM classifier.In GA, Integer and Binary Coded Genetic Algorithm are also used to calculate classification accuracy and run time. Integer- Coded Genetic Algorithm is used to select important and relevant features for classification. Binary Coded Genetic Algorithm can be applied to many optimization problems which contains binary string for the variables.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 104 pp. Englisch. Codice articolo 9786139992072
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Feature Selection using Genetic Algorithm to improve SVM Classifier
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LAP LAMBERT Academic Publishing, 2019
ISBN 10: 6139992079
ISBN 13: 9786139992072
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Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This book gives a classification algorithms like Support Vector Machine and Genetic Algorithm are used to find the classification accuracy for the Wisconsin Breast Cancer dataset. The benchmark dataset, Wisconsin Breast Cancer dataset is obtained from UCI Machine Learning Repository. The dataset consists of 699 instances divided into 2 classes namely Benign and Malignant, each with 11 attributes. Support vector machines (SVMs) are a set of related supervised learning methods used for classification. A classification SVM model attempts to separate the target classes with the widest possible margin. In SVM, Radial basis function and Polynomial kernel function are used to calculate classification accuracy and run time. Feature Selection is used to improve the accuracy of the SVM classifier.In GA, Integer and Binary Coded Genetic Algorithm are also used to calculate classification accuracy and run time. Integer- Coded Genetic Algorithm is used to select important and relevant features for classification. Binary Coded Genetic Algorithm can be applied to many optimization problems which contains binary string for the variables. Codice articolo 9786139992072
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Feature Selection using Genetic Algorithm to improve SVM Classifier
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ISBN 10: 6139992079
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Taschenbuch. Condizione: Neu. Feature Selection using Genetic Algorithm to improve SVM Classifier | Nithya Devaraj | Taschenbuch | 104 S. | Englisch | 2019 | LAP LAMBERT Academic Publishing | EAN 9786139992072 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu. Codice articolo 115353836
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Feature Selection using Genetic Algorithm to improve SVM Classifier
Editore:
LAP LAMBERT Academic Publishing, 2019
ISBN 10: 6139992079
ISBN 13: 9786139992072
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Condizione: Sehr gut. Zustand: Sehr gut | Sprache: Englisch | Produktart: Bücher | This book gives a classification algorithms like Support Vector Machine and Genetic Algorithm are used to find the classification accuracy for the Wisconsin Breast Cancer dataset. The benchmark dataset, Wisconsin Breast Cancer dataset is obtained from UCI Machine Learning Repository. The dataset consists of 699 instances divided into 2 classes namely Benign and Malignant, each with 11 attributes. Support vector machines (SVMs) are a set of related supervised learning methods used for classification. A classification SVM model attempts to separate the target classes with the widest possible margin. In SVM, Radial basis function and Polynomial kernel function are used to calculate classification accuracy and run time. Feature Selection is used to improve the accuracy of the SVM classifier.In GA, Integer and Binary Coded Genetic Algorithm are also used to calculate classification accuracy and run time. Integer- Coded Genetic Algorithm is used to select important and relevant features for classification. Binary Coded Genetic Algorithm can be applied to many optimization problems which contains binary string for the variables. Codice articolo 33557482/2
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Feature Selection using Genetic Algorithm to improve SVM Classifier
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LAP LAMBERT Academic Publishing, 2019
ISBN 10: 6139992079
ISBN 13: 9786139992072
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paperback. Condizione: New. NEW. SHIPS FROM MULTIPLE LOCATIONS. book. Codice articolo ERICA82961399920796
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