TY - GEN
T1 - Improving pattern recognition using several feature vectors
AU - Villela, Patricia Rayón
AU - Azuela, Juan Humberto Sossa
N1 - Publisher Copyright:
© Springer-Verlag Berlin Heidelberg 2002.
PY - 2002
Y1 - 2002
N2 - Most pattern recognition systems use only one feature vector to describe the objects to be recognized. In this paper we suggest to use more than one feature vector to improve the classification results. The use of several feature vectors require a special neural network, a supervised ART2 NN is used [1]. The performance of a supervised or unsupervised ART2 NN depends on the appropriate selection of the vigilance threshold. If the value is near to zero, a lot of clusters will be generated, but if it is greater, then must clusters will be generated. A methodology to select this threshold was first proposed in [2]. The advantages to use several feature vectors instead of only one are shown on this work. We show some results in the case of character recognition using one and two feature vectors. We also compare the performance of our proposal with the multilayer perceptron.
AB - Most pattern recognition systems use only one feature vector to describe the objects to be recognized. In this paper we suggest to use more than one feature vector to improve the classification results. The use of several feature vectors require a special neural network, a supervised ART2 NN is used [1]. The performance of a supervised or unsupervised ART2 NN depends on the appropriate selection of the vigilance threshold. If the value is near to zero, a lot of clusters will be generated, but if it is greater, then must clusters will be generated. A methodology to select this threshold was first proposed in [2]. The advantages to use several feature vectors instead of only one are shown on this work. We show some results in the case of character recognition using one and two feature vectors. We also compare the performance of our proposal with the multilayer perceptron.
KW - Digit recognition
KW - Multilayer perceptron
KW - Pattern recognition
KW - Supervised ART2 neural network
UR - http://www.scopus.com/inward/record.url?scp=84942900154&partnerID=8YFLogxK
M3 - Contribución a la conferencia
SN - 3540434755
SN - 9783540434757
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 282
EP - 291
BT - MICAI 2002
A2 - Battistutti, Osvaldo Cairo
A2 - Sucar, Luis Enrique
A2 - de Albornoz, Alvaro
A2 - Coello Coello, Carlos A.
PB - Springer Verlag
T2 - 2nd Mexican International Conference on Artificial Intelligence, MICAI 2002
Y2 - 22 April 2002 through 26 April 2002
ER -