Logistic regression is presumably the most popular representative of probabilistic discriminative classifiers. In this paper, a kernel variant of logistic regression is introduced as an iteratively re-weighted least-squares algorithm inkernel-induced feature spaces. This formulation allows us to apply highly efficient approximation methods that are capable of dealing with large-scale problems. For multi-class problems, a pairwise coupling procedure is proposed. Pairwise coupling for “kernelized” logistic regression effectively overcomes conceptual and numerical problems of standard multi-class kernel classifiers.
CITATION STYLE
Roth, V. (2001). Probabilistic discriminative kernel classifiers for multi-class problems. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2191, pp. 246–253). Springer Verlag. https://doi.org/10.1007/3-540-45404-7_33
Mendeley helps you to discover research relevant for your work.