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Traditionally, kernel learning methods require positive definitiveness on the kernel, which is too strict and excludes many sophisticated similarities, that are indefinite. To utilize those indefinite kernels, indefinite learning methods are of great interests. This paper aims at the extension of the logistic regression from positive definite kernels to indefinite ones. The proposed model, named indefinite kernel logistic regression (IKLR), keeps consistency to the regular KLR in formulationdoi:10.1145/3123266.3123295 dblp:conf/mm/LiuHY17 fatcat:fvofrgswtvfovbsv3gvochoy4m