2016/11/16 by Ehsan Sadrfaridpour, Sadrfaridpour, Ehsan, Sandeep Jeereddy +9
Computer Science · Mathematics · #Advanced Data Compression Techniques #Computation (stat.CO) #Data Structures and Algorithms (cs.DS) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and ELM #Tensor decomposition and applications
paper · pdf · doi:10.48550/arxiv.1611.05487
openalex publication_date 2016/11/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The support vector machine is a flexible optimization-based technique widely used for classification problems. In practice, its training part becomes computationally expensive on large-scale data sets because of such reasons as the complexity and number of iterations in parameter fitting methods, underlying optimization solvers, and nonlinearity of kernels. We introduce a fast multilevel framework for solving support vector machine models that is inspired by the algebraic multigrid. Significant improvement in the running has been achieved without any loss in the quality. The proposed technique is highly beneficial on imbalanced sets. We demonstrate computational results on publicly available and industrial data sets.