Letter identification is an important visual task for both practical and theoretical reasons. To extend and test existing models, we have reviewed published data for contrast sensitivity for letter identification as a function of size and have also collected new data. Contrast sensitivity increases rapidly from the acuity limit but slows and asymptotes at a symbol size of about 1 degree. We rec...

This paper introduces a new classifier, that is based on fuzzy-integration schemes controlled by a genetic optimisation procedure. Two different types of integration are proposed here, and are validated by experiments on real data sets of biological cells. The performance of our classifier is tested against a feed-forward neural network and a Support Vector Machine. Results show the good perfor...

Neural network ensemble techniques have been shown to be very accurate classification techniques. However, in some real-life applications a number of classifiers required to achieve a reasonable accuracy is enormously large and hence very space consuming. This paper proposes several methods for pruning neural network ensembles. The clustering based approach applies k-means clustering to entire ...

Learning Classifier Systems have traditionally used a binary representation, with wildcards added to facilitate generalization. As they are applied to more complex domains the simple representation can become limiting. In this paper we present results from the use of a neural network-based representation scheme within the accuracy-based XCS. Here each rule’s condition and action are represented...

Text Mining is around applying knowledge discovery techniques to unstructured text is termed knowledge discovery in text (KDT), or Text data mining or Text Mining. In Neural Network that address classification problems, training set, testing set, learning rate are considered as key tasks. That is collection of input/output patterns that are used to train the network and used to assess the netwo...

Pitch detection based on neural-net classifiers is investigated. T o this end, the extent of generalization attainable with neural nets is first examined, and i t is shown t h a t a suitable choice of features is required t o utilize this property. Specifically, invaria n t features should be used whenever possible. For pitch detection, two feature sets, one based on waveform samples and the ot...

In this paper, we show that the LVQ learning algorithm converges to locally asymptotic stable equilibria of an ordinary differential equation. We show that the learning algorithm performs stochastic approximation. Convergence of the Voronoi vectors is guaranteed under the appropriate conditions on the underlying statistics of the classification problem. We also present a modification to the lea...

This article presents a real-time Fuzzy ART neural classifier for skin segmentation implemented on a Graphics Processing Unit (GPU). GPUs have evolved into powerful programmable processors, becoming increasingly used in time-dependent research fields such as dynamics simulation, database management, computer vision or image processing. GPUs are designed following a Stream Processing Model and e...

Thomas Kailath Inform. Systems Lab. Stanford University Stanford, Calif. 94305 A rigorous analysis on the finite precision computational <)Spects of neural network as a pattern classifier via a probabilistic approach is presented. Even though there exist negative results on the capability of perceptron, we show the following positive results: Given n pattern vectors each represented by en bits ...