Error-Aware Density-Based Clustering of Imprecise Measurement Values

摘要

The purpose of this paper is to introduce new approaches for kernel logistic regression (KLR) in a semi-supervised setting. Using the special structure of Laplacian kernel matrices, we propose new formulations which minimize the negative log likelihood of