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Advances in Large Margin Classifiers
Advances in Large Margin Classifiers
A Tutorial on Support Vector Machines for Pattern Recognition
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CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
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The Journal of Machine Learning Research
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ICML '04 Proceedings of the twenty-first international conference on Machine learning
Style Context with Second-Order Statistics
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Determining the Number of Clusters/Segments in Hierarchical Clustering/Segmentation Algorithms
ICTAI '04 Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence
The Minimum Error Minimax Probability Machine
The Journal of Machine Learning Research
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IEEE Transactions on Neural Networks
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
Mercer kernel-based clustering in feature space
IEEE Transactions on Neural Networks
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ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks
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RSFDGrC '09 Proceedings of the 12th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
IPCM separability ratio for supervised feature selection
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Analysis of the distance between two classes for tuning SVM hyperparameters
IEEE Transactions on Neural Networks
Optimal Locality Regularized Least Squares Support Vector Machine via Alternating Optimization
Neural Processing Letters
Support vector machines based on weighted scatter degree
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part III
Information Sciences: an International Journal
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PRICAI'12 Proceedings of the 12th Pacific Rim international conference on Trends in Artificial Intelligence
Three-fold structured classifier design based on matrix pattern
Pattern Recognition
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Knowledge-Based Systems
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Knowledge-Based Systems
Integrated Fisher linear discriminants: An empirical study
Pattern Recognition
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This paper proposes a new large margin classifier--the structured large margin machine (SLMM)--that is sensitive to the structure of the data distribution. The SLMM approach incorporates the merits of "structured" learning models, such as radial basis function networks and Gaussian mixture models, with the advantages of "unstructured" large margin learning schemes, such as support vector machines and maxi-min margin machines. We derive the SLMM model from the concepts of "structured degree" and "homospace", based on an analysis of existing structured and unstructured learning models. Then, by using Ward's agglomerative hierarchical clustering on input data (or data mappings in the kernel space) to extract the underlying data structure, we formulate SLMM training as a sequential second order cone programming. Many promising features of the SLMM approach are illustrated, including its accuracy, scalability, extensibility, and noise tolerance. We also demonstrate the theoretical importance of the SLMM model by showing that it generalizes existing approaches, such as SVMs and M4s, provides novel insight into learning models, and lays a foundation for conceiving other "structured" classifiers.