A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Hierarchical mixtures of experts and the EM algorithm
Neural Computation
Classification by pairwise coupling
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Support Vector Mixture for Classification and Regression Problems
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 1 - Volume 1
Training linear SVMs in linear time
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Invariant Object Material Identification via Discriminant Learning on Absorption Features
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
Mixture of Support Vector Machines for HMM based Speech Recognition
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
Working Set Selection Using Second Order Information for Training Support Vector Machines
The Journal of Machine Learning Research
Adaptive mixtures of local experts
Neural Computation
Mixing linear SVMs for nonlinear classification
IEEE Transactions on Neural Networks
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In this paper, we propose a new method for training mixtures of linear SVM classifiers for purposes of non-linear data classification. We do this by packaging linear SVMs into a probabilistic formulation and embedding them in the mixture of experts model. The weights of the mixture model are generated by the gating network dependent on the input data. The new mixture of linear SVMs can be then trained efficiently using the EM algorithm. Unlike previous SVM-based mixture of expert models, which use a divide-and-conquer strategy to reduce the burden of training for large scale data sets, the main purpose of our approach is to improve the efficiency for testing. Experimental results show that our proposed model can achieve the efficiency of linear classifiers in the prediction phase while still maintaining the classification performance of nonlinear classifiers.