Vector quantization and signal compression
Vector quantization and signal compression
The nature of statistical learning theory
The nature of statistical learning theory
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Tabu Search
Choosing Multiple Parameters for Support Vector Machines
Machine Learning
Classifying large data sets using SVMs with hierarchical clusters
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
In Defense of One-Vs-All Classification
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
Dimension Reduction in Text Classification with Support Vector Machines
The Journal of Machine Learning Research
Core Vector Machines: Fast SVM Training on Very Large Data Sets
The Journal of Machine Learning Research
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
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
An EA multi-model selection for SVM multiclass schemes
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
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In this paper, a new learning method is proposed to build Support Vector Machines (SVMs) Binary Decision Functions (BDF) of reduced complexity and efficient generalization. The aim is to build a fast and efficient SVM classifier. A criterion is defined to evaluate the Decision Function Quality (DFQ) which blendes recognition rate and complexity of a BDF. Vector Quantization (VQ) is used to simplify the training set. A model selection based on the selection of the simplification level, of a feature subset and of SVM hyperparameters is performed to optimize the DFQ. Search space for selecting the best model being huge, Tabu Search (TS) is used to find a good sub-optimal model on tractable times. Experimental results show the efficiency of the method.