https://sci2s.ugr.es/sites/default/files/files/TematicWebSites/pr/1995-Kuncheva-PRL.pdf

Editing for the k -nearest neighbors rule by a genetic algorithm

Pattern recognition with a model of fuzzy neuron using degree of consensus

In this paper we consider a radial basis function (RBF) network with tunable shape and spreadout parameters of the activation function. We argue that fewer hidden nodes with different RBF shapes can better match the classification regions still preserving the context of the probabilistic semiparametric approximation of the conditional probability density functions (pdf). Instead of squared Euclidean norm (L/sub 2/ norm) in the power of the exponent, a weighted L/sub p/ norm is used with variable p. In order to demonstrate the advantage of the proposed scheme some experimental results on the 2-spirals data set are reported.

An RBF network with tunable function shape

Initializing of an RBF network by a genetic algorithm

A neural network architecture that is used to classify a set of patterns into one of a set of known classes is described. The network is comprised of a set of trainable neural processing units (neurons) that have an elliptical activation function and a set of adaptable connections. A fast training algorithm is provided for the network, which guarantees that all elements of an arbitrary training set can be correctly learned by the network in finite time. To demonstrate the network's ability to train, and its ability to quickly generalize and classify noisy test data, a network is developed to classify degraded omnifont alphanumeric machine printed characters. Using a training set of over 69000 characters and a separate test set of over 36000 characters, a classification accuracy of 97.5% with an average network throughput of 211 characters per second is achieved. >

Design of an elliptical neural network with application to degraded character classification

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Design of an elliptical neural network with application to degraded character classification