Similarity-based methods: a general framework for classifications, approximation and association

Similarity-based methods (SBM) are a generalization of the minimal distance (MD) methods, which form the basis of several machine learning and pattern recognition methods. Investigation of similarity leads to a fruitful framework in which many classification, approximation and association methods are accommodated. Probability p(C|X;M) of assigning class C to a vector X; given a classification model M, depends on adaptive parameters and procedures used in construction of the model. Systematic overview of choices available for model building is presented and numerous improvements suggested. Similarity-Based Methods have natural neural network type realizations. Such neural network models as the Radial Basis Function (RBF) and the Multilayer Perceptrons (MLPs) are included in this framework as special cases.