Modifying Desired Outputs to Improve Pattern Recognition by Combining Subfeature-Input Neural Networks

Neural Pattern Recognition on Multichannel Input Representation

This article presents a new neural pattern recognition architecture on multichannel data representation. The architecture emploies generalized ART modules as building blocks to construct a supervised learning system generating recognition codes on channels dynamically selected in context using serial and parallel match trackings led by inter-ART vigilance signals.

Pattern Recognition Using Neural Networks

Pattern Recognition and Neural Networks

Improving Optical Music Recognition by Combining Outputs from Multiple Sources

Current software for Optical Music Recognition (OMR) produces outputs with too many errors that render it an unrealistic option for the production of a large corpus of symbolic music files. In this paper, we propose a system which applies image pre-processing techniques to scans of scores and combines the outputs of different commercial OMR programs when applied to images of different scores of...

Pattern recognition with neural networks combined by genetic algorithm

Soft computing techniques have been recently exploited as a promising tool for achieving high performance in pattem recognition. This paper presents a hybrid method which combines neural network classifiers by genetic algorithm. Genetic algorithm gives us an effective vehicle to determine the optimal weight parameters that are multiplied by the network outputs as coefficients. The experimental ...