Classification & Optimization to Evaluate the Fitness of an Algorithm: An Application of Biologically Inspired Neural Networks for Classification with Evolutionary Algorithm for Optimization - Brossura
Sinossi
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.
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L'autore
Prof. 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 pursuing Ph.D. in Information Fusion (Ensemble) Techniques in Data Warehousing and Data Mining from KSV University, Gandhinagar.
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Classification & Optimization to Evaluate the Fitness of an Algorithm
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LAP LAMBERT Academic Publishing Mrz 2012, 2012
ISBN 10: 3848419939
ISBN 13: 9783848419937
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Taschenbuch. Condizione: 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. Codice articolo 9783848419937
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Classification & Optimization to Evaluate the Fitness of an Algorithm
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LAP LAMBERT Academic Publishing, 2012
ISBN 10: 3848419939
ISBN 13: 9783848419937
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Condizione: 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. Codice articolo 5520872
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Classification & Optimization to Evaluate the Fitness of an Algorithm
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LAP LAMBERT Academic Publishing Mär 2012, 2012
ISBN 10: 3848419939
ISBN 13: 9783848419937
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Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. 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.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 56 pp. Englisch. Codice articolo 9783848419937
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Classification & Optimization to Evaluate the Fitness of an Algorithm : An Application of Biologically Inspired Neural Networks for Classification with Evolutionary Algorithm for Optimization
Editore:
LAP LAMBERT Academic Publishing, 2012
ISBN 10: 3848419939
ISBN 13: 9783848419937
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Taschenbuch. Condizione: 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. Codice articolo 9783848419937
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Classification & Optimization to Evaluate the Fitness of an Algorithm | An Application of Biologically Inspired Neural Networks for Classification with Evolutionary Algorithm for Optimization
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LAP LAMBERT Academic Publishing, 2012
ISBN 10: 3848419939
ISBN 13: 9783848419937
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Taschenbuch. Condizione: Neu. Classification & Optimization to Evaluate the Fitness of an Algorithm | An Application of Biologically Inspired Neural Networks for Classification with Evolutionary Algorithm for Optimization | Amit Ganatra (u. a.) | Taschenbuch | 56 S. | Englisch | 2012 | LAP LAMBERT Academic Publishing | EAN 9783848419937 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu Print on Demand. Codice articolo 106572405
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Classification & Optimization to Evaluate the Fitness of an Algorithm: An Application of Biologically Inspired Neural Networks for Classification with Evolutionary Algorithm for Optimization
Editore:
LAP LAMBERT Academic Publishing, 2012
ISBN 10: 3848419939
ISBN 13: 9783848419937
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