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Making large-scale support vector machine learning practical
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Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Automatically tuned linear algebra software
SC '98 Proceedings of the 1998 ACM/IEEE conference on Supercomputing
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Efficient SVM Regression Training with SMO
Machine Learning
Convergence of a Generalized SMO Algorithm for SVM Classifier Design
Machine Learning
Training Invariant Support Vector Machines
Machine Learning
A theory of complexity for continuous time systems
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A parallel mixture of SVMs for very large scale problems
Neural Computation
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Neural Computation
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
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Improvements to Platt's SMO Algorithm for SVM Classifier Design
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Neural Computation
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Fast and Scalable Local Kernel Machines
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Image and Vision Computing
Fuzzy integral to speed up support vector machines training for pattern classification
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IEEE Transactions on Image Processing
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Neurocomputing
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Expert Systems with Applications: An International Journal
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Expert Systems with Applications: An International Journal
Active learning for sparse least squares support vector machines
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Training support vector machines with multiple equality constraints
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Support vector machine for large databases as classifier
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Fast instance selection for speeding up support vector machines
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Fast classification for large data sets via random selection clustering and Support Vector Machines
Intelligent Data Analysis
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Training a support vector machine on a data set of huge size with thousands of classes is a challenging problem. This paper proposes an efficient algorithm to solve this problem. The key idea is to introduce a parallel optimization step to quickly remove most of the nonsupport vectors, where block diagonal matrices are used to approximate the original kernel matrix so that the original problem can be split into hundreds of subproblems which can be solved more efficiently. In addition, some effective strategies such as kernel caching and efficient computation of kernel matrix are integrated to speed up the training process. Our analysis of the proposed algorithm shows that its time complexity grows linearly with the number of classes and size of the data set. In the experiments, many appealing properties of the proposed algorithm have been investigated and the results show that the proposed algorithm has a much better scaling capability than Libsvm, {\rm{SVM}}^{light}, and {\rm{SVMTorch}}. Moreover, the good generalization performances on several large databases have also been achieved.