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Machine Learning Approach Dimensionality by Srivastava Namita (5 results)
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Machine Learning Approach: For dimensionality reduction of microarray data
Published by LAP LAMBERT Academic Publishing, 2020
ISBN 10: 620056843X ISBN 13: 9786200568434
Language: English
Condition: New.
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Machine Learning Approach: For dimensionality reduction of microarray data
Published by LAP LAMBERT Academic Publishing, 2020
ISBN 10: 620056843X ISBN 13: 9786200568434
Language: English
Paperback. Condition: Brand New. 152 pages. 8.66x5.91x0.35 inches. In Stock.
-
Machine Learning Approach: For dimensionality reduction of microarray data
Published by LAP LAMBERT Academic Publishing, 2020
ISBN 10: 620056843X ISBN 13: 9786200568434
Language: English
Condition: New. Print on Demand.
-
Machine Learning Approach: For dimensionality reduction of microarray data
Published by LAP LAMBERT Academic Publishing, 2020
ISBN 10: 620056843X ISBN 13: 9786200568434
Language: English
Condition: New. PRINT ON DEMAND.
-
Machine Learning Approach : For dimensionality reduction of microarray data
Published by LAP LAMBERT Academic Publishing, 2020
ISBN 10: 620056843X ISBN 13: 9786200568434
Language: English
Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - For past several years, microarray technology has attracted tremendous interest for both scientific community and industry. Recently, the applications of microarrays include gene discovery, disease diagnosis and prognosis, drug discovery, etc. High dimensional data with small sample size is the main problem that generate the application of dimension reduction in microarray data analysis. It is seen that SVM, ANN and NB have recently gained wide popularity for cancer classification problems. An efficient and reliable method of dimension reduction plays an important role to improve the performance of SVM, ANN and NB, when applied for classification of high dimensional microarray data. In this book, we applied different combinations of feature selection / extraction methods, as a novel hybrid dimension reduction method for SVM, ANN and NB classifiers. The obtained results are compared with other popular published dimension reduction methods for SVM, NB and ANN classifiers.
