A neural-network-based classifier for large pattern recognition

Title:

A neural-network-based classifier for large pattern recognition

Creator:

Aligué, Francisco Javier López, Macías, Miguel Macías, Velasco, Horacio Manuel González, Orellana, Carlos Javier García, and Sotoca, María Isabel Acevedo

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:c16c169d-4964-45cc-bd64-6a64e103c358
uuid:c16c169d-4964-45cc-bd64-6a64e103c358

Subject:

neural function, training algorithm, pattern classification, and NIST SD19 database

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

In this paper a classification system, which corisists of a neural network and a decision element, is presented, both parts processing information in series. For the neural network, we propose a training algorithm based on the direct equalization of weights and components of prototype vectors, and a neuronal function that detects similarities between its inputs and the weights. This systematics allows, in addition to a good performance in recognition, an easy, time-controlled reprogramming process of the network, even for large patterns. To test and validate the system, a real classifier is presented and studied, a classifier that is designed to recognize segmented handwritten characters corresponding to the NIST SD19 database and with which good results for digits and lower-case letters are obtained.

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Source:

Neural network world: international journal on neural and mass-parallel computing and information systems | 2003 Volume:13 | Number:1

Harvested from:

CDK

Metadata only:

false