id: 05971983
dt: j
an: 05971983
au: Maalouf, Maher; Trafalis, Theodore B.; Adrianto, Indra
ti: Kernel logistic regression using truncated Newton method.
so: Comput. Manag. Sci. 8, No. 4, 415-428 (2011).
py: 2011
pu: Springer, Heidelberg
la: EN
cc: 
ut: classification
ci: 
li: doi:10.1007/s10287-010-0128-1
ab: Summary: 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.
rv: