A GPU-tailored approach for training kernelized SVMs
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Asynchronous peer-to-peer data mining with stochastic gradient descent
Euro-Par'11 Proceedings of the 17th international conference on Parallel processing - Volume Part I
Large Linear Classification When Data Cannot Fit in Memory
ACM Transactions on Knowledge Discovery from Data (TKDD)
Review: Supervised classification and mathematical optimization
Computers and Operations Research
Automatic Korean word spacing using Pegasos algorithm
Information Processing and Management: an International Journal
Separable approximate optimization of support vector machines for distributed sensing
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part II
Dependency-based semantic role labeling using sequence labeling with a structural SVM
Pattern Recognition Letters
Searching informative concept banks for video event detection
Proceedings of the 3rd ACM conference on International conference on multimedia retrieval
MI2LS: multi-instance learning from multiple informationsources
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Instance Annotation for Multi-Instance Multi-Label Learning
ACM Transactions on Knowledge Discovery from Data (TKDD) - Special Issue on ACM SIGKDD 2012
Clickage: towards bridging semantic and intent gaps via mining click logs of search engines
Proceedings of the 21st ACM international conference on Multimedia
The Journal of Machine Learning Research
Entity disambiguation in anonymized graphs using graph kernels
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Large-scale linear nonparallel support vector machine solver
Neural Networks
Premise Selection for Mathematics by Corpus Analysis and Kernel Methods
Journal of Automated Reasoning
Hi-index | 0.00 |
We describe and analyze a simple and effective stochastic sub-gradient descent algorithm for solving the optimization problem cast by Support Vector Machines (SVM). We prove that the number of iterations required to obtain a solution of accuracy $${\epsilon}$$ is $${\tilde{O}(1 / \epsilon)}$$, where each iteration operates on a single training example. In contrast, previous analyses of stochastic gradient descent methods for SVMs require $${\Omega(1 / \epsilon^2)}$$ iterations. As in previously devised SVM solvers, the number of iterations also scales linearly with 1/λ, where λ is the regularization parameter of SVM. For a linear kernel, the total run-time of our method is $${\tilde{O}(d/(\lambda \epsilon))}$$, where d is a bound on the number of non-zero features in each example. Since the run-time does not depend directly on the size of the training set, the resulting algorithm is especially suited for learning from large datasets. Our approach also extends to non-linear kernels while working solely on the primal objective function, though in this case the runtime does depend linearly on the training set size. Our algorithm is particularly well suited for large text classification problems, where we demonstrate an order-of-magnitude speedup over previous SVM learning methods.