In this paper, we study the recognition complexity of discrete geometric 2gures (rectangles, squares, circles, ellipses) on a retina by diameter-limited and order-restricted perceptrons. We construct a diameter-limited recognition perceptron for the family of rectangles, beginning with local con2gurations, which is di6erent from the one shown by Minsky et al. (Perceptrons: An Introduction to Co...

We study the capabilities of two-layered perceptrons for classifying exactly a given subset. Both necessary and sufficient conditions are derived for subsets to be exactly classifiable with two-layered perceptrons that use the hard-limiting response function. The necessary conditions can be viewed as generalizations of the linear-separability condition of one-layered perceptrons and confirm the...

As the size of samples grows, the accuracy of trained multi-layer perceptrons grows with some improvement in error rates. But we cannot use larger and larger samples, because computational complexity to train the multi-layer perceptrons becomes enormous and data overfitting problem can happen. This paper suggests an effective approach in determining a proper sample size for multi-layer perceptr...

We study a relation between Hadamard powers and polynomial kernel perceptrons. The rank of for the special case Boolean matrix generic real is computed explicitly. These results are interpreted in terms classification capacities

Neural networks are organized in committees to improve the correctness of the decisions created by artificial neural networks (ANN’s). In the classification of human chromosomes, it is accustomed to use multilayer perceptrons with multiple (22-24) outputs. Because of the huge number of synaptic weights to be tuned, these classifiers cannot go beyond a level of 92% overall correctness. In this s...

In this paper we conduct a comparative study between hybrid methods to optimize multilayer perceptrons: a model that optimizes the architecture and initial weights of multilayer perceptrons; a parallel approach to optimize the architecture and initial weights of multilayer perceptrons; a method that searches for the parameters of the training algorithm, and an approach for cooperative co-evolut...