Classification & Optimization to Evaluate the Fitness of an Algorithm: An Application of Biologically Inspired Neural Networks for Classification with Evolutionary Algorithm for Optimization - Couverture souple

Ganatra, Amit; Panchal, Gaurang; Gajjar, Chintan

 
9783848419937: Classification & Optimization to Evaluate the Fitness of an Algorithm: An Application of Biologically Inspired Neural Networks for Classification with Evolutionary Algorithm for Optimization
Couverture souple
ISBN 10 : 3848419939 ISBN 13 : 9783848419937
Editeur : LAP LAMBERT Academic Publishing, 2012

Synopsis

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.

Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.

Présentation de l'éditeur

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.

Biographie de l'auteur

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.

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

Éditeur
LAP LAMBERT Academic Publishing
Date d'édition
2012
Langue
anglais
ISBN 10 
3848419939
ISBN 13 
9783848419937
Reliure
Broché
Nombre de pages
56
Coordonnées du fabricant
non disponible
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