Classification, Association and Pattern Completion using Neural Similarity Based Methods

Abstract: A framework for Similarity-Based Methods (SBMs) includes many classification models as special cases: neural network of the Radial Basis Function Networks type, Feature Space Mapping neurofuzzy networks based on separable transfer functions, Learning Vector Quantization, variants of the k nearest neighbor methods and several new models that may be presented in a network form. Multilayer Perceptrons (MLPs) use scalar products to compute weighted activation of neurons, combining soft hyperplanes to provide decision borders. Distance-based multilayer perceptrons (D-MLPs) evaluate similarity of inputs to weights offering a natural generalization of standard MLPs. Cluster-based initialization procedure determining architecture and values of all adaptive parameters is described. Networks implementing SBM methods are useful not only for classification and approximation, but also as associative memories, in problems requiring pattern completion, offering an efficient way to deal with missing values. Non-Euclidean distance functions may also be introduced by normalization of the input vectors in an extended feature space. Both approaches influence shapes of decision borders dramatically. An illustrative example showing these changes is provided.