Support Vector Machines and the Bayes Rule in Classification
Data Mining and Knowledge Discovery
A Generalized Representer Theorem
COLT '01/EuroCOLT '01 Proceedings of the 14th Annual Conference on Computational Learning Theory and and 5th European Conference on Computational Learning Theory
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Normally the microarray data contain a large number of genes (usually more than 1000) and a relatively small number of samples (usually fewer than 100). This makes the discriminant analysis of DNA microarray data hard to handle. Selecting important genes to the discriminant problem is hence of much practically significance in microarray data analysis. If put in the context of pattern classification, gene selection can be casted as a feature selection problem. Feature selection approaches are broadly grouped into filter and wrapper methods. The wrapper method outperforms the filter method in general. However the accuracy of wrapper methods is coupled with intensive computations. In present study, we proposed a wrapper-based gene selection algorithm by employing the Regularization Network as the classifier. Compared with classical wrapper method, the computational costs in our gene selection algorithm is significantly reduced, because the evaluation criterion we used does not demand repeated trainings in the leave-one-out procedure.