Speaker Diarization for Conference Room: The UPC RT07s Evaluation System
Multimodal Technologies for Perception of Humans
A Learning Algorithm of Boosting Kernel Discriminant Analysis for Pattern Recognition
IEICE - Transactions on Information and Systems
Support vector machine classification based on fuzzy clustering for large data sets
MICAI'06 Proceedings of the 5th Mexican international conference on Artificial Intelligence
Fast pixel classification by SVM using vector quantization, tabu search and hybrid color space
CAIP'05 Proceedings of the 11th international conference on Computer Analysis of Images and Patterns
A new model selection method for SVM
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
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In this paper, we describe a new method for training SVM on large data sets. Vector Quantization is applied to reduce a large data set by replacing examples by prototypes. Training time for choosing optimal parameters is greatly reduced. Some experimental results yields to demonstrate that this method can reduce training time by a factor of 100, while preserving classification rate. Moreover this method allows to find a decision function with a low complexity when the training data set includes noisy or error examples.