Solving multiclass support vector machines with LaRank

Abstract : 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 ...
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Antoine Bordes, Léon Bottou, Patrick Gallinari, Jason Weston. Solving multiclass support vector machines with LaRank. ICML 2007 - 24th International Conference on Machine Learning, Jun 2007, Corvallis, United States. pp.89--96, ⟨10.1145/1273496.1273508⟩. ⟨hal-00750277⟩



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