Catalogo Articoli (Spogli Riviste)

OPAC HELP

Titolo:
A closed-form neural network for discriminatory feature extraction from high-dimensional data
Autore:
Talukder, A; Casasent, D;
Indirizzi:
Carnegie Mellon Univ, Dept Elect & Comp Engn, Pittsburgh, PA 15213 USA Carnegie Mellon Univ Pittsburgh PA USA 15213 gn, Pittsburgh, PA 15213 USA CALTECH, Jet Prop Lab, Pasadena, CA 91109 USA CALTECH Pasadena CA USA 91109 LTECH, Jet Prop Lab, Pasadena, CA 91109 USA
Titolo Testata:
NEURAL NETWORKS
fascicolo: 9, volume: 14, anno: 2001,
pagine: 1201 - 1218
SICI:
0893-6080(200111)14:9<1201:ACNNFD>2.0.ZU;2-L
Fonte:
ISI
Lingua:
ENG
Soggetto:
PRINCIPAL COMPONENT ANALYSIS; NONLINEAR PCA; REPRESENTATION; CLASSIFICATION;
Keywords:
dimensionality reduction; discrimination; feature extraction (nonlinear); neural networks; nonlinear transform; pattern recognition;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Engineering, Computing & Technology
Citazioni:
38
Recensione:
Indirizzi per estratti:
Indirizzo: Casasent, D Carnegie Mellon Univ, Dept Elect & Comp Engn, Pittsburgh, PA 15213 USA Carnegie Mellon Univ Pittsburgh PA USA 15213 gh, PA 15213 USA
Citazione:
A. Talukder e D. Casasent, "A closed-form neural network for discriminatory feature extraction from high-dimensional data", NEURAL NETW, 14(9), 2001, pp. 1201-1218

Abstract

We consider a new neural network for data discrimination in pattern recognition applications. We refer to this as a maximum discriminating feature (MDF) neural network. Its weights are obtained in closed-form, thereby overcoming problems associated with other nonlinear neural networks. It uses neuron activation functions that are dynamically chosen based on the application. It is theoretically shown to provide nonlinear transforms of the input data that are more general than those provided by other nonlinear multilayerperceptron neural network and support-vector machine techniques for cases involving high-dimensional (image) inputs where training data are limited and the classes are not linearly separable. We experimentally verify this onsynthetic examples. (C) 2001 Elsevier Science Ltd. All rights reserved.

ASDD Area Sistemi Dipartimentali e Documentali, Università di Bologna, Catalogo delle riviste ed altri periodici
Documento generato il 08/04/20 alle ore 09:01:48