Vector quantization and signal compression
Vector quantization and signal compression
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Tabu Search
Computer Vision and Image Understanding
SVMTorch: support vector machines for large-scale regression problems
The Journal of Machine Learning Research
SVM Training Time Reduction using Vector Quantization
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
An EA multi-model selection for SVM multiclass schemes
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
Machine learning to design full-reference image quality assessment algorithm
Image Communication
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, a new learning method is proposed to build Support Vector Machines (SVM) Binary Decision Function (BDF) of reduced complexity, efficient generalization and using an adapted hybrid color space. The aim is to build a fast and efficient SVM classifier of pixels. The Vector Quantization (VQ) is used in our learning method to simplify the training set. This simplification step maps pixels of the training set to representative prototypes. A criterion is defined to evaluate the Decision Function Quality (DFQ) which blends recognition rate and complexity of a BDF. A model selection based on the selection of the simplification level, of a hybrid color space and of SVM hyperparameters is performed to optimize this DFQ. Search space for selecting the best model being huge. Our learning method uses Tabu Search (TS) metaheuritics to find a good sub-optimal model on tractable times. Experimental results show the efficiency of the method.