… In section (2), we briefly review some results of the SVM for binary classification and
compare it with kernel logistic regression (KLR). In section (3), we propose our IVM algorithm. In …

… a fraction of the training data to index the kernel basis functions. We call these training data
… for two-class classification and compare it with kernel logistic regression (KLR). In Section 3, …

… This paper gives a new iterative algorithm for kernel logistic regression. It is based on the
solution of a dual problem using ideas similar to those of the Sequential Minimal Optimization …

… Literature review shows that many advanced machine learning techniques such as kernel
logistic regression and logistic model trees have seldom been explored for landslide modeling…

… kernel logistic regression In this section the derivation of the kernel version of multi-class
logistic regression … advanced with a nonlinear extension to kernel machines where the inputs x …

… a novel kernel logistic regression (KLR) method based on diffusion kernels for protein … In
this study, we develop a new diffusion kernel-based logistic regression (KLR) model to predict …

… This research combines rare events corrections to Logistic Regression (LR) … to Kernel
Logistic Regression (KLR). The resulting model, Rare Event Weighted Kernel Logistic Regression (…

… robust Kernel Logistic Regression (rKLR) as an extension of the robust Logistic Regression
(… We then propose a robust Multiple Kernel Logistic Regression algorithm (rMKLR) based on …

… performance by formulation without heuristics and task dependence, we propose
confusion-matrix-based kernel logistic regression (CM-KLOGR). Its objective function is the harmonic …

… 2 Kernel logistic regression In this section, we provide a brief overview of the kernel logistic
regression (KLR) model, and introduce the notation used throughout. In an off-line statistical …