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On Using Dimensionality Reduction Schemes to Optimize Dissimilarity-Based Classifiers
[chapter]
2008
Lecture Notes in Computer Science
The aim of this paper is to present a strategy by which a new philosophy for pattern classification pertaining to dissimilarity-based classifiers (DBCs) can be efficiently implemented. Proposed by Duin and his co-authors, DBCs are a way of defining classifiers among classes; they are not based on the feature measurements of individual patterns, but rather on a suitable dissimilarity measure among the patterns. The problem with this strategy is that we need to select a representative set of data
doi:10.1007/978-3-540-85920-8_38
fatcat:4ior3nbivjdk5hc5lkd3dxzw6e