This paper presents a self-organizing hierarchical cerebellar model arithmetic computer (HCMAC) neural-network classifier, which contains a self-organizing input space module and an HCMAC neural network. The conventional CMAC can be viewed as a basis function network (BFN) with supervised learning, and performs well in terms of its fast learning speed and local generalization capability for app...

Convolutional neural networks (CNNs) have been widely applied in the computer vision community to solve complex problems in image recognition and analysis. We describe an application of the CNN technology to the problem of identifying particle interactions in sampling calorimeters used commonly in high energy physics and high energy neutrino physics in particular. Following a discussion of the ...

In this paper, we explore the neural network as a disease classifier. In our investigation, the sets of parameters describing glaucomatous and healthy eyes are taken. These sets represent the structure of the optical nerve disc which resides in a patient’s eye fundus image. As a separate case, the excavation can be seen in the image as well. These two sets describe the elliptical shape of both ...

The assignment of natural language texts to one or more predefined categories based on their content – is an important component in many information organization and management tasks. This research proposes a novel approach for documents classification with using novel method that combined competitive self organizing neural text categorizer with new vectors that we called, string vectors. Even ...

Learning Classifier Systems (LCS) are populationbased reinforcement learners used in a wide variety of applications. This paper presents a LCS where each traditional rule is represented by a spiking neural network, a type of network with dynamic internal state. We employ a constructivist model of growth of both neurons and dendrites that realise flexible learning by evolving structures of suffi...

INTRODUCTION An Artificial Neural Network (ANN) is a computational structure inspired by the study of biological neural processing. Although neurons are considered as very simple computation units, inside the nervous system, an incredible amount of widely interconnected neurons can process huge amounts of data working in a parallel fashion. There are many different types of ANNs, from relativel...

In this paper, we introduce a neural network-based decision table algorithm. We focus on the implementation details of the decision table algorithm when it is constructed using the neural network. Decision tables are simple supervised classifiers which, Kohavi demonstrated, can outperform state-of-the-art classifiers such as C4.5. We couple this power with the efficiency and flexibility of a bi...

This article describes a new approach to the automated construction of a distributed neural classifier. The methodology is based upon supervised hierarchical clustering which enables one to determine reliable regions in the representation space. The proposed methodology proceeds by associating each of these regions with a Multi-Layer Perceptron (MLP). Each MLP has to recognise elements inside i...