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Superfast-Trainable Multi-Class Probabilistic Classifier by Least-Squares Posterior Fitting
2010
IEICE transactions on information and systems
Kernel logistic regression (KLR) is a powerful and flexible classification algorithm, which possesses an ability to provide the confidence of class prediction. However, its training-typically carried out by (quasi-)Newton methods-is rather time-consuming. In this paper, we propose an alternative probabilistic classification algorithm called Least-Squares Probabilistic Classifier (LSPC). KLR models the class-posterior probability by the log-linear combination of kernel functions and its
doi:10.1587/transinf.e93.d.2690
fatcat:ywnz7kdisndjrkvr4iqep37yli