Comparing Prediction Accuracy for Machine Learning - Setu Kar - Libros - LAP LAMBERT Academic Publishing - 9783659557330 - 12 de junio de 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.

Medios de comunicación Libros     Paperback Book   (Libro con tapa blanda y lomo encolado)
Publicado 12 de junio de 2014
ISBN13 9783659557330
Editores LAP LAMBERT Academic Publishing
Páginas 124
Dimensiones 152 × 229 × 7 mm   ·   203 g
Lengua Alemán