Kernel logistic regression using truncated Newton method

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.