Quadratic kernel-free non-linear support vector machine
Journal of Global Optimization
A Facial Expression Recognition Approach Based on Novel Support Vector Machine Tree
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III
Spanning SVM Tree for Personalized Transductive Learning
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part I
Curiosity driven incremental LDA agent active learning
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Differential Evolution for learning the classification method PROAFTN
Knowledge-Based Systems
Expert Systems with Applications: An International Journal
Factorizing class characteristics via group MEBs construction
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: models and applications - Volume Part II
Personalized mode transductive spanning SVM classification tree
Information Sciences: an International Journal
Two-Class SVM trees (2-SVMT) for biomarker data analysis
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
Automatic parameter settings for the PROAFTN classifier using hybrid particle swarm optimization
AI'10 Proceedings of the 23rd Canadian conference on Advances in Artificial Intelligence
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This paper presents a new membership authentication method by face classification using a support vector machine (SVM) classification tree, in which the size of membership group and the members in the membership group can be changed dynamically. Unlike our previous SVM ensemble-based method, which performed only one face classification in the whole feature space, the proposed method employed a divide and conquer strategy that first performs a recursive data partition by membership-based locally linear embedding (LLE) data clustering, then does the SVM classification in each partitioned feature subset. Our experimental results show that the proposed SVM tree not only keeps the good properties that the SVM ensemble method has, such as a good authentication accuracy and the robustness to the change of members, but also has a considerable improvement on the stability under the change of membership group size.