Comparing Prediction Accuracy For Machine Learning - Setu Kar
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Piegāde 12-18 darba dienu laikā
30 dienu atgriešanas politika
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 c ... Pilns apraksts
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Aprašymas
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.
Vairāk informācijas
| Autors | Setu Kar |
|---|---|
| Izdevējs | LAP LAMBERT Academic Publishing |
| Izlaides gads | 2014 |
| Vāka tips | Mīkstais vāks |
| EAN | 9783659557330 |