Pattern recognition: statistical, structural and neural approaches
Pattern recognition: statistical, structural and neural approaches
Neural networks: algorithms, applications, and programming techniques
Neural networks: algorithms, applications, and programming techniques
Pattern recognition using neural networks: theory and algorithms for engineers and scientists
Pattern recognition using neural networks: theory and algorithms for engineers and scientists
Pattern Recognition with Neural Network in C++
Pattern Recognition with Neural Network in C++
A Procedure to Select the Vigilance Threshold for the ART2 for Supervised and Unsupervised Training
MICAI '00 Proceedings of the Mexican International Conference on Artificial Intelligence: Advances in Artificial Intelligence
Face recognition from a single image per person: A survey
Pattern Recognition
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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.