2005 | OriginalPaper | Buchkapitel
Nonlinear Kernel MSE Methods for Cancer Classification
verfasst von : L. Shen, E. C. Tan
Erschienen in: Advances in Natural Computation
Verlag: Springer Berlin Heidelberg
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Combination of kernel PLS (KPLS) and kernel SVD (KSVD) with minimum-squared-error (MSE) criteria has created new machine learning methods for cancer classification and has been successfully applied to seven publicly available cancer datasets. Besides the high accuracy of the new methods, very fast training speed is also obtained because the matrix inversion in the original MSE procedure is avoided. Although the KPLS-MSE and the KSVD-MSE methods have equivalent accuracies, the KPLS achieves the same results using significantly less but more qualitative components.