Divide-and-Conquer Kernel SVM
- A fast and scalable classification software
DC-SVM implements a divide-and-conquer procedure for speeding up kernel SVM training.
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DCSVM_codeCitation
This software is released under the BSD License but please acknowledge its use with a citation to the following publication:- A Divide-and-Conquer Solver for Kernel Support Vector Machines (pdf, software, code)
C. Hsieh, S. Si, I. Dhillon.
In International Conference on Machine Learning (ICML), pp. 566-574, June 2014.
Additional Information
Example on ijcnn1 dataset (included in the package)
>> demo_ijcnn Start training Gaussian kernel SVM with early prediction RBF kernel, DCSVM-early test accuracy 0.983959, training time 5.57 seconds Start training Gaussian kernel SVM Training Level 4 Training Level 3 Training Level 2 Training Level 1 RBF kernel, DC-SVM test accuracy 0.983915, training time 31.44 seconds Start training polynomial kernel SVM with early prediction polynomial kernel, DC-SVM early test accuracy 0.985998, training time 29.59 seconds Start training polynomial kernel SVM Training Level 4 Training Level 3 Training Level 2 Training Level 1 polynomial kernel, DC-SVM test accuracy 0.971189, training time 43.45 seconds