RANSAC-SVM for large-scale datasets

Support Vector Machines (SVMs), though accurate, are still difficult to solve large-scale applications, due to the computational and storage requirement. To relieve this problem, we propose RANSAC-SVM method, which trains a number of small SVMs for randomly selected subsets of training set, while tuning their parameters to fit SVMs to whole training set. RANSAC-SVM achieves good generalization performance, which close to the Bayesian estimation, with small subset of the training samples, and outperforms the full SVM solution in some condition.

• Type: Conference
• Year: 2008
• Where: ICPR
• Authors: Kenji Watanabe, Takio Kurita
• Keywords: Computer Vision, ICPR 2008, Small Svms, Support Vector Machines, Training Set