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Publicado por LAP Lambert Academic Publishing, 2012
ISBN 10: 3848419939ISBN 13: 9783848419937
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 2012-03, 2012
ISBN 10: 3848419939ISBN 13: 9783848419937
Librería: Chiron Media, Wallingford, Reino Unido
Libro
PF. Condición: New.
Publicado por LAP LAMBERT Academic Publishing, 2012
ISBN 10: 3848419939ISBN 13: 9783848419937
Librería: Lucky's Textbooks, Dallas, TX, Estados Unidos de America
Libro
Condición: New.
Publicado por LAP LAMBERT Academic Publishing, 2012
ISBN 10: 3848447479ISBN 13: 9783848447473
Librería: Lucky's Textbooks, Dallas, TX, Estados Unidos de America
Libro
Condición: New.
Publicado por LAP LAMBERT Academic Publishing Mrz 2012, 2012
ISBN 10: 3848419939ISBN 13: 9783848419937
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 -In classifying large data set, efficiency and scalability are main issues. Advantages of neural networks include their high tolerance to noisy data, as well as their ability to classify patterns on which they have not been trained. Neural networks are a good choice for most classification and prediction tasks. The necessary complexity of neural networks is one of the most interesting problems in the research. One of the challenges in training MLP is in optimizing weight changes. Advances are introduced in traditional Back Propagation (BP) algorithm, to overcome its limitations. One method is to hybrid GA with BP to optimize weight changes.The objective here is to develop a data classification algorithm that will be used as a general-purpose classifier. To classify any database first, it is required to train the model. The proposed training algorithm used here is a Hybrid BP-GA. After successful training user can give unlabeled data to classify. 56 pp. Englisch.
Publicado por LAP LAMBERT Academic Publishing, 2012
ISBN 10: 3848419939ISBN 13: 9783848419937
Librería: PBShop.store US, Wood Dale, IL, Estados Unidos de America
Libro Impresión bajo demanda
PAP. Condición: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
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, 2012
ISBN 10: 3848419939ISBN 13: 9783848419937
Librería: AHA-BUCH GmbH, Einbeck, Alemania
Libro Impresión bajo demanda
Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - In classifying large data set, efficiency and scalability are main issues. Advantages of neural networks include their high tolerance to noisy data, as well as their ability to classify patterns on which they have not been trained. Neural networks are a good choice for most classification and prediction tasks. The necessary complexity of neural networks is one of the most interesting problems in the research. One of the challenges in training MLP is in optimizing weight changes. Advances are introduced in traditional Back Propagation (BP) algorithm, to overcome its limitations. One method is to hybrid GA with BP to optimize weight changes.The objective here is to develop a data classification algorithm that will be used as a general-purpose classifier. To classify any database first, it is required to train the model. The proposed training algorithm used here is a Hybrid BP-GA. After successful training user can give unlabeled data to classify.
Publicado por LAP LAMBERT Academic Publishing, 2012
ISBN 10: 3848419939ISBN 13: 9783848419937
Librería: PBShop.store UK, Fairford, GLOS, Reino Unido
Libro Impresión bajo demanda
PAP. Condición: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
Publicado por LAP LAMBERT Academic Publishing Okt 2013, 2013
ISBN 10: 3659395870ISBN 13: 9783659395871
Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania
Libro Impresión bajo demanda
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, 2012
ISBN 10: 3848419939ISBN 13: 9783848419937
Librería: moluna, Greven, Alemania
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Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Ganatra AmitProf. Amit P. Ganatra is concurrently holding Associate Professor (Jan 2010 till date), Headship in Computer Engineering Department at C.S.P.I.T., CHARUSAT and Deanship in Faculty of Technology-CHARUSAT, Gujarat and he is.
Publicado por LAP LAMBERT Academic Publishing, 2012
ISBN 10: 3848447479ISBN 13: 9783848447473
Librería: moluna, Greven, Alemania
Libro Impresión bajo demanda
Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Panchal GaurangProf. Gaurang Panchal is Currently working as Assistant Professor at Charotar University of Science and Technology, Changa, India.His area of interest includes Soft Computing, Artificial Intelligence and Biometrics.His.
Publicado por LAP Lambert Academic Publishing, 2013
ISBN 10: 3659395870ISBN 13: 9783659395871
Librería: Ria Christie Collections, Uxbridge, Reino Unido
Libro Impresión bajo demanda
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
Libro
PF. Condición: New.
Publicado por LAP LAMBERT Academic Publishing, 2013
ISBN 10: 3659395870ISBN 13: 9783659395871
Librería: PBShop.store US, Wood Dale, IL, Estados Unidos de America
Libro Impresión bajo demanda
PAP. Condición: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
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.
Publicado por LAP LAMBERT Academic Publishing, 2013
ISBN 10: 3659395870ISBN 13: 9783659395871
Librería: PBShop.store UK, Fairford, GLOS, Reino Unido
Libro Impresión bajo demanda
PAP. Condición: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
Publicado por LAP LAMBERT Academic Publishing, 2012
ISBN 10: 3848419939ISBN 13: 9783848419937
Librería: Mispah books, Redhill, SURRE, Reino Unido
Libro
Paperback. Condición: Like New. Like New. book.
Publicado por LAP LAMBERT Academic Publishing, 2012
ISBN 10: 3848447479ISBN 13: 9783848447473
Librería: dsmbooks, Liverpool, Reino Unido
Libro
Paperback. Condición: Like New. Like New. book.