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
Least Squares Support Vector Machine Classifiers
Neural Processing Letters
Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models
Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Support vector fuzzy regression machines
Fuzzy Sets and Systems - Theme: Learning and modeling
Credit rating analysis with support vector machines and neural networks: a market comparative study
Decision Support Systems - Special issue: Data mining for financial decision making
Support Vector Machines: Theory and Applications (Studies in Fuzziness and Soft Computing)
Support Vector Machines: Theory and Applications (Studies in Fuzziness and Soft Computing)
Neural Computation
Fuzzy output support vector machines for classification
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part III
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
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Support vector machine (SVM), proposed by Vapnik based on statistical learning theory, is a novel machine learning method. However, there are two problems to be solved in this field: one is the multi-class classification problem, and the other is the sensitivity to the noisy data. In order to overcome these difficulties, a novel method of fuzzy compensation multi-class support vector machine, named as FC-SVM, is proposed in this paper. This method imports a fuzzy compensation function to the penalty in the straightly construction multi-class SVM classification problem proposed by Weston and Watkins. Aim at the dual affects to classification results by each input data, this method has punish item be fuzzy, compensates weight to classification, reconstructs the optimization problem and its restrictions, reconstructs Langrage formula, and presents the theories deduction. This method is applied to the benchmark data sets. The experiment presents our method is feasible.