An efficient and scalable data compression approach to classification
ACM SIGKDD Explorations Newsletter - Special issue on “Scalable data mining algorithms”
Local Learning Framework for Recognition of Lowercase Handwritten Characters
MLDM '01 Proceedings of the Second International Workshop on Machine Learning and Data Mining in Pattern Recognition
An Efficient Data Compression Approach to the Classification Task
PAKDD '01 Proceedings of the 5th Pacific-Asia Conference on Knowledge Discovery and Data Mining
Texture classification using sparse frame-based representations
EURASIP Journal on Applied Signal Processing
Borderline detection by Bayes vector quantizers
Proceedings of the 2008 ACM symposium on Applied computing
Developing an open knowledge discovery support system for network environment
CTS'05 Proceedings of the 2005 international conference on Collaborative technologies and systems
KDD support services based on data semantics
Journal on Data Semantics IV
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In pattern classification, a decision rule is a labeled partition of the observation space, where labels represent classes. A way to establish a decision rule is to attach a label to each code vector of a vector quantizer (VQ). When a labeled VQ is adopted as a classifier, we have to design it in such a way that high classification performance is obtained by a given number of code vectors. In this paper we propose a learning algorithm which optimizes the position of labeled code vectors in the observation space under the minimum average misclassification risk criterion