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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 - Softcover
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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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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- PublisherLAP LAMBERT Academic Publishing
- Publication date2012
- ISBN 10 3848419939
- ISBN 13 9783848419937
- BindingPaperback
- Number of pages56
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Classification & Optimization to Evaluate the Fitness of an Algorithm
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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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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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Book Description Taschenbuch. Condition: 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. Seller Inventory # 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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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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Book Description Taschenbuch. Condition: 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. Seller Inventory # 9783848419937
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Book Description Condition: 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. Seller Inventory # 5520872
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