Comparing Prediction Accuracy for Machine Learning - Setu Kar - Books - LAP LAMBERT Academic Publishing - 9783659557330 - June 12, 2014
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Comparing Prediction Accuracy for Machine Learning

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Classification is one of the most important tasks for different application such as text categorization, tone recognition, image classification, micro-array gene expression, proteins structure predictions, data classification etc. Microarray based gene expression profiling has been emerged as an efficient technique for cancer classification, as well as for diagnosis, prognosis, and treatment purposes. One challenging area in the studies of gene expression data is the classification of different types of tumors into correct classes. Diagonal discriminant analysis, regularized discriminant analysis, support vector machines and k-nearest neighbor have been suggested as among the best methods for small sample size situations. The methods are applied to datasets from four recently published cancer gene expression studies. This book is really helpful for understanding the prediction accuracy of some supervised algorithms.

Media Books     Paperback Book   (Book with soft cover and glued back)
Released June 12, 2014
ISBN13 9783659557330
Publishers LAP LAMBERT Academic Publishing
Pages 124
Dimensions 152 × 229 × 7 mm   ·   203 g
Language German