LaRank is an online solver for multiclass Support Vector Machines.
Optimization algorithms for large margin multiclass recognizers are often too costly to handle ambitious problems with structured outputs and exponential numbers of classes. Optimization algorithms that rely on the full gradient are not effective because, unlike the solution, the gradient is not sparse and is very large.
The LaRank algorithm sidesteps this difficulty by relying on a randomized exploration inspired by the perceptron algorithm. We show that this approach is competitive with gradient based optimizers on simple multiclass problems. Furthermore, a single LaRank pass over the training examples delivers test error rates that are nearly as good as those of the final solution.
- Changes to previous version:
Initial Announcement on mloss.org.
- BibTeX Entry: Download
- Corresponding Paper BibTeX Entry: Download
- URL: Project Homepage
- Supported Operating Systems: Linux, Macosx, Unix
- Data Formats: None
- Tags: Svm, Classification, Support Vector Machines, Online Learning, Kernel Methods, Multi Class, Gradient Based Learning, Large Scale Learning, Machine Learning, Algorithms
- Archive: download here
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