Indefinite Kernel Logistic Regression With Concave-Inexact-Convex Procedure.

Fanghui Liu,Xiaolin Huang,Chen Gong,Jie Yang,Johan A K Suykens

IEEE Transactions on Neural Networks and Learning Systems（2019）

引用 15|浏览32

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摘要

In kernel methods, the kernels are often required to be positive definitethat restricts the use of many indefinite kernels. To consider those nonpositive definite kernels, in this paper, we aim to build an indefinite kernel learning framework for kernel logistic regression (KLR). The proposed indefinite KLR (IKLR) model is analyzed in the reproducing kernel Kreĭn spaces and then becomes nonconvex....

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关键词

Kernel,Stochastic processes,Logistics,Optimization,Pollution measurement,Support vector machines,Convergence

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