The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Machine Learning
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Training Support Vector Machines: an Application to Face Detection
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
Multicategory nets of single-layer perceptrons: complexity and sample-size issues
IEEE Transactions on Neural Networks
Multiclass mineral recognition using similarity features and ensembles of pair-wise classifiers
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part II
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This paper presents a problem independent weighting strategy for weighted support vector machines (SVMs). SVMs can be applied with a weighting to each training vector to reflect the importance of different classes or training samples. Weightings are often assigned to the two classes inversely proportional to the sample count of each class, or according to a priori knowledge. Such a strategy can be applied to skewed data sets to balance the importance, error contribution and cost between the two classes. In this paper we propose a strategy to give each training pattern a weighting according to their distances to the classifier. The strategy regards the importance of the training patterns to the training process but not the importance of the data to the problem, thus it is suitable for general SVM applications. Experiments show that the performance of the proposed method is competitive to standard SVM while the training processes are even sped up.