Classification, Association and Pattern Completion Using Neural Similarity Based Methods

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. Network

Neural Networks from Similarity Based Perspective

A framework for Similarity-Based Methods (SBMs) includes many neural network models as special cases. Multilayer Perceptrons (MLPs) use scalar products to compute weighted activation of neurons, combining soft hyperplanes to provide decision borders. Scalar product is replaced by a distance function between the inputs and the weights, offering a natural generalization of the standard MLP model to the distance-based multilayer perceptron (D-MLP) model. D-MLPs evaluate similarity of inputs to weights making the interpretation of their mappings easier. Cluster-based initialization procedure determining architecture and values of all adaptive parameters is described. D-MLP networks 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 fe..

Distance-based Multilayer Perceptrons

Neural network models are presented as special cases of a framework for general Similarity-Based Methods (SBMs). Distance-based multilayer perceptrons (D-MLPs) with non-Euclidean metric functions are described. D-MLPs evaluate similarity to prototypes making the interpretation of the results easier. Renormalization of the input data in the extended feature space brings dramatic changes in the shapes of decision borders. An illustrative example showing these changes is provided