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Publicado por LAP LAMBERT Academic Publishing, 2013
ISBN 10: 3659395870ISBN 13: 9783659395871
Librería: Lucky's Textbooks, Dallas, TX, Estados Unidos de America
Libro
Condición: New.
Publicado por LAP LAMBERT Academic Publishing Okt 2013, 2013
ISBN 10: 3659395870ISBN 13: 9783659395871
Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania
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Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Information fusion is becoming a major need in Data Mining. Typical applications of these techniques include data modeling (ensemble methods). The behavior of various classification algorithms differs based on accuracy and computational complexity. For some algorithms there may be a significant variation in the performance when some parameters are varied. In this research the behavior of the modified AdaBoost algorithm with NN as a base classifier and as a preprocessing step feature selection combined with the evaluation schemas (like subset evaluation, consistency based, correlation based, filter approach, wrapper approach etc.) are applied by varying the number of parameters. Predictive accuracy is substantially improved when combining multiple predictors. A novel idea of an Ensemble System applying Boosting to Neural Networks for High Dimensional Datasets. The method uses Genetic Algorithms (to select relevant features) for essential feature selection with various Evaluation Schemes. As Genetic Algorithms deal well with large solution spaces, tuning it to adjust as per the requirements of the ensemble, we can get optimum feature selection. Finally Boosting algorithm that finishe 200 pp. Englisch.
Publicado por LAP Lambert Academic Publishing, 2013
ISBN 10: 3659395870ISBN 13: 9783659395871
Librería: Ria Christie Collections, Uxbridge, Reino Unido
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Condición: New. PRINT ON DEMAND Book; New; Fast Shipping from the UK. No. book.
Publicado por LAP Lambert Academic Publishing 2013-10, 2013
ISBN 10: 3659395870ISBN 13: 9783659395871
Librería: Chiron Media, Wallingford, Reino Unido
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PF. Condición: New.
Publicado por LAP LAMBERT Academic Publishing, 2013
ISBN 10: 3659395870ISBN 13: 9783659395871
Librería: AHA-BUCH GmbH, Einbeck, Alemania
Libro Impresión bajo demanda
Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Information fusion is becoming a major need in Data Mining. Typical applications of these techniques include data modeling (ensemble methods). The behavior of various classification algorithms differs based on accuracy and computational complexity. For some algorithms there may be a significant variation in the performance when some parameters are varied. In this research the behavior of the modified AdaBoost algorithm with NN as a base classifier and as a preprocessing step feature selection combined with the evaluation schemas (like subset evaluation, consistency based, correlation based, filter approach, wrapper approach etc.) are applied by varying the number of parameters. Predictive accuracy is substantially improved when combining multiple predictors. A novel idea of an Ensemble System applying Boosting to Neural Networks for High Dimensional Datasets. The method uses Genetic Algorithms (to select relevant features) for essential feature selection with various Evaluation Schemes. As Genetic Algorithms deal well with large solution spaces, tuning it to adjust as per the requirements of the ensemble, we can get optimum feature selection. Finally Boosting algorithm that finishe.
Publicado por LAP LAMBERT Academic Publishing, 2013
ISBN 10: 3659395870ISBN 13: 9783659395871
Librería: moluna, Greven, Alemania
Libro
Condición: New.