Kernel logistic regression using truncated Newton method |
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Authors: | Maher Maalouf Theodore B Trafalis Indra Adrianto |
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Institution: | 1. School of Industrial Engineering, University of Oklahoma, 202 WestBoyd, Room 124, Norman, OK, 73019, USA
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Abstract: | Kernel logistic regression (KLR) is a powerful nonlinear classifier. The combination of KLR and the truncated-regularized iteratively re-weighted least-squares (TR-IRLS) algorithm, has led to a powerful classification method using small-to-medium size data sets. This method (algorithm), is called truncated-regularized kernel logistic regression (TR-KLR). Compared to support vector machines (SVM) and TR-IRLS on twelve benchmark publicly available data sets, the proposed TR-KLR algorithm is as accurate as, and much faster than, SVM and more accurate than TR-IRLS. The TR-KLR algorithm also has the advantage of providing direct prediction probabilities. |
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