2017/06/19 by Carlos M. Alaíz, Johan A. K. Suykens, Alaíz, Carlos M. +1
Computer Science · Mathematics · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Face and Expression Recognition #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and ELM #Neural Networks and Applications #cs.AI #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.1706.05928
openalex publication_date 2017/06/19 · arxiv created 2018/04/13 · arxiv updated 2018/04/16 · openalex created_date 2022/10/01 · openalex updated_date 2026/07/28
This work proposes a new algorithm for training a re-weighted L2 Support Vector Machine (SVM), inspired on the re-weighted Lasso algorithm of Candès et al. and on the equivalence between Lasso and SVM shown recently by Jaggi. In particular, the margin required for each training vector is set independently, defining a new weighted SVM model. These weights are selected to be binary, and they are automatically adapted during the training of the model, resulting in a variation of the Frank-Wolfe optimization algorithm with essentially the same computational complexity as the original algorithm. As shown experimentally, this algorithm is computationally cheaper to apply since it requires less iterations to converge, and it produces models with a sparser representation in terms of support vectors and which are more stable with respect to the selection of the regularization hyper-parameter.