9786139864638 - a multi-objective particle swarm optimization for feature selection de roopa, y. mohana; babu, d.kishore (6 resultados)
- Tapa blanda
Librería: Revaluation Books, Exeter, Reino UnidoRevaluation Books
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 71,82
Envío por EUR 11,58Se envía de Reino Unido a Estados Unidos de AmericaCantidad disponible: 1 disponibles
Paperback. Condición: Brand New. 60 pages. 8.66x5.91x0.14 inches. In Stock.
- Tapa blanda
Librería: preigu, Osnabrück, Alemaniapreigu
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 36,25
Envío por EUR 70,00Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: 5 disponibles
Taschenbuch. Condición: Neu. A Multi-Objective Particle Swarm Optimization For Feature Selection | Y. Mohana Roopa (u. a.) | Taschenbuch | 60 S. | Englisch | 2018 | LAP LAMBERT Academic Publishing | EAN 9786139864638 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]d…e | Anbieter: preigu.
- Tapa blanda
- Impresión bajo demanda
Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, AlemaniaBuchWeltWeit Ludwig Meier e.K.
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 39,90
Envío por EUR 23,00Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: 2 disponibles
Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Feature selection is very important task in classification. Number of features is available for classification but not all of them are useful. Irrelevant and redundant features may even reduce the performance. There are two types of… feature selection approaches. They are wrapper and filter approaches. Their main difference is that wrappers use a classification algorithm when searching the goodness of the features during the feature selection process while filters are independent of any classification algorithm. The goal of Feature selection is to choose a small number of relevant features to achieve similar or even better classification performance than using all features. Existing feature selection algorithms treat the task as a single objective problem. The proposed system can treat as a multi objective problem. It has two objectives. They are maximizing the classification performance and minimizing the number of features. The proposed system is PSO-based multi-objective feature selection algorithm. The algorithm (NSPSOFS) introduces the task is to generate a Pareto front of non dominated solutions idea of non dominated sorting into PSO to address feature selection problems. 60 pp. Englisch.
- Tapa blanda
- Impresión bajo demanda
Librería: moluna, Greven, Alemaniamoluna
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 34,25
Envío por EUR 48,99Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: Más de 20 disponibles
Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Roopa Y. MohanaDr. Y Mohana Roopa is the Professor of Computer science and Engineering. She is also Dean of Continuing Education and Internal Quality Assurance at Institute of Aeronautical Engineering.…She has 18 years of experience .
- Tapa blanda
- Impresión bajo demanda
Librería: buchversandmimpf2000, Emtmannsberg, Alemaniabuchversandmimpf2000
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 39,90
Envío por EUR 60,00Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: 1 disponibles
Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Feature selection is very important task in classification. Number of features is available for classification but not all of them are useful. Irrelevant and redundant features may even reduce the performance. There are two types of fea…ture selection approaches. They are wrapper and filter approaches. Their main difference is that wrappers use a classification algorithm when searching the goodness of the features during the feature selection process while filters are independent of any classification algorithm. The goal of Feature selection is to choose a small number of relevant features to achieve similar or even better classification performance than using all features. Existing feature selection algorithms treat the task as a single objective problem. The proposed system can treat as a multi objective problem. It has two objectives. They are maximizing the classification performance and minimizing the number of features. The proposed system is PSO-based multi-objective feature selection algorithm. The algorithm (NSPSOFS) introduces the task is to generate a Pareto front of non dominated solutions idea of non dominated sorting into PSO to address feature selection problems.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 60 pp. Englisch.
- Tapa blanda
- Impresión bajo demanda
Librería: AHA-BUCH GmbH, Einbeck, AlemaniaAHA-BUCH GmbH
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 40,89
Envío por EUR 60,54Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: 1 disponibles
Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Feature selection is very important task in classification. Number of features is available for classification but not all of them are useful. Irrelevant and redundant features may even reduce the performance. There are two types of feat…ure selection approaches. They are wrapper and filter approaches. Their main difference is that wrappers use a classification algorithm when searching the goodness of the features during the feature selection process while filters are independent of any classification algorithm. The goal of Feature selection is to choose a small number of relevant features to achieve similar or even better classification performance than using all features. Existing feature selection algorithms treat the task as a single objective problem. The proposed system can treat as a multi objective problem. It has two objectives. They are maximizing the classification performance and minimizing the number of features. The proposed system is PSO-based multi-objective feature selection algorithm. The algorithm (NSPSOFS) introduces the task is to generate a Pareto front of non dominated solutions idea of non dominated sorting into PSO to address feature selection problems.
