Discovering informative patterns and data cleaning
Advances in knowledge discovery and data mining
Support Vector Machines for 3D Object Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Support Vector Machines: Theory and Applications (Studies in Fuzziness and Soft Computing)
Support Vector Machines: Theory and Applications (Studies in Fuzziness and Soft Computing)
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
Evaluating Membership Functions for Fuzzy Discrete SVM
WILF '07 Proceedings of the 7th international workshop on Fuzzy Logic and Applications: Applications of Fuzzy Sets Theory
FSVM-CIL: fuzzy support vector machines for class imbalance learning
IEEE Transactions on Fuzzy Systems - Special section on computing with words
Regularization through fuzzy discrete SVM with applications to customer ranking
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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A new Fuzzy Support Vector Machines (λ—FSVMs) based on λ—cut is proposed in this paper. The proposed learning machines combine the membership of fuzzy set with support vector machines. The λ—cut set is introduced to distinguish the training samples set in term of the importance of the data. The more important sets are selected as new training sets to construct the fuzzy support vector machines. The benchmark two-class problems and multi-class problems datasets are used to test the effectiveness and validness of λ—FSVMs. The experiment results indicate that λ—FSVMs not only has higher precision but also solves the overfitting problem of the support vector machines more effectively.