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Titolo:
HANDWRITTEN CHARACTER CLASSIFICATION USING NEAREST-NEIGHBOR IN LARGE DATABASES
Autore:
SMITH SJ; BOURGOIN MO; SIMS K; VOORHEES HL;
Indirizzi:
THINKING MACHINES CORP,245 1ST ST CAMBRIDGE MA 02142 TASC READING MA 01867
Titolo Testata:
IEEE transactions on pattern analysis and machine intelligence
fascicolo: 9, volume: 16, anno: 1994,
pagine: 915 - 919
SICI:
0162-8828(1994)16:9<915:HCCUNI>2.0.ZU;2-L
Fonte:
ISI
Lingua:
ENG
Soggetto:
DISTANCE;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
CompuMath Citation Index
Science Citation Index Expanded
Science Citation Index Expanded
Citazioni:
22
Recensione:
Indirizzi per estratti:
Citazione:
S.J. Smith et al., "HANDWRITTEN CHARACTER CLASSIFICATION USING NEAREST-NEIGHBOR IN LARGE DATABASES", IEEE transactions on pattern analysis and machine intelligence, 16(9), 1994, pp. 915-919

Abstract

We show that systems built on a simple statistical technique and a large training database can be automatically optimized to produce classification accuracies of 99% in the domain of handwritten digits. It is also shown that the performance of these systems scale consistently with the size of the training database, where the error rate is cut by more than half for every tenfold increase in the size of the training set from 10 to 100,000 examples. Three distance metrics for the standard Nearest Neighbor classification system are investigated: a simple Hamming distance metric, a pixel distance metric, and a metric based on the extraction of penstroke features. Systems employing these metrics were trained and tested on a standard, publicly available, database ofnearly 225,000 digits provided by the National Institute of Standardsand Technology. Additionally, a confidence metric is both introduced by the authors and also discovered and optimized by the system. The new confidence measure proves to be superior to the commonly used Nearest Neighbor distance.

ASDD Area Sistemi Dipartimentali e Documentali, Università di Bologna, Catalogo delle riviste ed altri periodici
Documento generato il 27/11/20 alle ore 21:05:45