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Beyond Traditional Kernels: Classification in Two Dissimilarity-Based Representation Spaces
2008
IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews)
Proximity captures the degree of similarity between examples and is thereby fundamental in learning. Learning from pairwise proximity data usually relies on either kernel methods for specifically designed kernels or the nearest neighbor (NN) rule. Kernel methods are powerful, but often cannot handle arbitrary proximities without necessary corrections. The NN rule can work well in such cases, but suffers from local decisions. The aim of this paper is to provide an indispensable explanation and
doi:10.1109/tsmcc.2008.2001687
fatcat:olsjte6j7zan3gtvqhpwyl7dhm