The dissimilarity representation for finding universals from particulars by an anti-essentialist approach
The dissimilarity representation for designing pattern recognition systems is analyzed for its ability to build new knowledge from examples using an anti-essentialist approach. It is argued that it may find universals (pattern classifiers) from particulars (training set of examples) but that the resulting knowledge can just be applied but not accessed. Consequently, its use for a conscious human decision maker is limited.
representation anti-essentialism generalization nearest neighbor rule