Abstract. Selective attention shift can help neural networks learn invariance. We describe a method that can produce a network with invariance to changes in visual input caused by ...
Abstract. In this paper we derive an upper bound for the average-case generalization error of the mixture of experts modular neural network, based on an average-case generalization...
In this paper, we propose a novel approach for evolving the architecture of a multi-layer neural network. Our method uses combined ART1 algorithm and Max-Min neural network to self...
In this paper, we describe an adaptive approach to gesture for musical applications. Neural Network abstractions and interfaces are implemented in the Pure Data environment which ...
A flexible neural network (FNN) is a multilayer feedforward neural network with the characteristics of: (1) overlayer connections; (2) variable activation functions for different...
Abstract. The aim of our research was to apply well-known data mining techniques (such as linear neural networks, multi-layered perceptrons, probabilistic neural networks, classifi...
Marcin Paprzycki, Ajith Abraham, Ruiyuan Guo, Srin...
Neural networks have played an important role in intelligent medical diagnoses. This paper presents an Improved Constructive Neural Network Ensemble (ICNNE) approach to three medic...
In order for a neural network ensemble to generalise properly, two factors are considered vital. One is the diversity and the other is the accuracy of the networks that comprise th...
Nonlinear state estimation is a useful approach to the monitoring of industrial (polymerization) processes. This paper investigates how this approach can be followed to the develop...
The identification of genes that influence the risk of common, complex diseases primarily through interactions with other genes and environmental factors remains a statistical and ...
Marylyn D. Ritchie, Christopher S. Coffey, Jason H...