Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
A deterministic annealing approach to clustering
Pattern Recognition Letters
Classified vector quantization using variance classifier and maximum likelihood clustering
Pattern Recognition Letters
Handwritten digit recognition using an optimized nearest neighbor classifier
Pattern Recognition Letters
Learning vector quantization with training count (LVQTC)
Neural Networks
Some Extensions of the K-Means Algorithm for Image Segmentation and Pattern Classification
Some Extensions of the K-Means Algorithm for Image Segmentation and Pattern Classification
Parameter extraction from population codes: A critical assessment
Neural Computation
Adaptive voting rules for k-nearest neighbors classifiers
Neural Computation
Neural-network classifiers for recognizing totally unconstrained handwritten numerals
IEEE Transactions on Neural Networks
Hybrid model of clustering and kernel autoassociator for reliable vehicle type classification
Machine Vision and Applications
| Hi-index | 0.00 |
Learning Vector Quantisation (LVQ) is a method of applying the Vector Quantisation (VQ) to generate references for Nearest Neighbour (NN) classification. Though successful in many occasions, LVQ suffers from several shortcomings, especially the reference vectors are prone to diverge. In this paper, we propose a Classified Vector Quantisation (CVQ) to establish VQ for classification. By CVQ, each data category is represented by its own codebook, which can be implemented by some learning algorithms. In classification process, each codebook offers a generalised NN. The examples of handwritten digit recognition and offline signature verification are used to demonstrate the efficiency of the proposed scheme.