LASVM is an approximate SVM solver that uses online approximation. It reaches accuracies similar to that of a real SVM after performing a single sequential pass through the training examples. Further benefits can be achieved using selective sampling techniques to choose which example should be considered next. As show in the graph, LASVM requires considerably less memory than a regular SVM solver. This becomes a considerable speed advantage for large training sets. In fact LASVM has been used to train a 10 class SVM classifier with 8 million examples on a single processor.
Keywords for this software
References in zbMATH (referenced in 3 articles )
Showing results 1 to 3 of 3.
- Gertz, E.Michael; Griffin, Joshua D.: Using an iterative linear solver in an interior-point method for generating support vector machines (2010)
- Collobert, Ronan; Sinz, Fabian; Weston, Jason; Bottou, Léon: Large scale transductive SVMs (2006)
- Bordes, Antoine; Ertekin, Seyda; Weston, Jason; Bottou, Léon: Fast kernel classifiers with online and active learning (2005)
Further publications can be found at: http://leon.bottou.org/papers