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Soft Computing for Machine Learning and Bioinformatics: An integrated approach for problem solving - Softcover
Synopsis
Hybridization using different soft computing tools has been explored for efficient data mining, clust- ering, and classification. An evolutionary-rough feature selection algorithm has been developed for feature selection and classi- fication of gene expre- ssion patterns. Next A detailed clustering algorithm is developed by integrating the advantage of both rough and fuzzy set theories. The remaining three chapters are devoted for biclustering problem. The whole work of this book is divided into the follow- ing: i) Mining important features from high dimen- sional gene datasets; ii) Biclustering or local stru- cture determination in gene expression data using soft tools; iii) Collaborative clustering for global structure determination in large data; iv) Covering various machine learning and bioinformatics applica- tions using soft computing; and v) A special chapter is written for designing a new paradigm for optimi- zation. The book focuses on some applications of the newly developed soft computing based methodologies for machine learning, optimization,and bioinformatic Problems. The book covers how to design a hybrid system for real life problem solving.
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About the Author
Dr. Haider Banka is an Assistant Professor in the Department of Computer Science and Engineering, Indian School of Mines, Dhan- bad, India. He has more than 7 years of teaching and Research Experience and published more than 18 research articles. His main research interest includes Soft Computing, M/c Learning, and Optimization.
"About this title" may belong to another edition of this title.
- PublisherVDM Verlag Dr. Müller
- Publication date2010
- ISBN 10 3639264843
- ISBN 13 9783639264845
- BindingPaperback
- LanguageEnglish
- Number of pages200
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Hybridization using different soft computing tools has been explored for efficient data mining, clust- ering, and classification. An evolutionary-rough feature selection algorithm has been developed for feature selection and classi- fication of gene expre- ssion patterns. Next A detailed clustering algorithm is developed by integrating the advantage of both rough and fuzzy set theories. The remaining three chapters are devoted for biclustering problem. The whole work of this book is divided into the follow- ing: i) Mining important features from high dimen- sional gene datasets; ii) Biclustering or local stru- cture determination in gene expression data using soft tools; iii) Collaborative clustering for global structure determination in large data; iv) Covering various machine learning and bioinformatics applica- tions using soft computing; and v) A special chapter is written for designing a new paradigm for optimi- zation. The book focuses on some applications of the newly developed soft computing based methodologies for machine learning, optimization,and bioinformatic Problems. The book covers how to design a hybrid system for real life problem solving. Seller Inventory # 9783639264845
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