Training linear SVMs in linear time

Thorsten Joachims
2006 Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '06  
Linear Support Vector Machines (SVMs) have become one of the most prominent machine learning techniques for highdimensional sparse data commonly encountered in applications like text classification, word-sense disambiguation, and drug design. These applications involve a large number of examples n as well as a large number of features N , while each example has only s << N non-zero features. This paper presents a Cutting-Plane Algorithm for training linear SVMs that provably has training time
more » ... sn) for classification problems and O(sn log(n)) for ordinal regression problems. The algorithm is based on an alternative, but equivalent formulation of the SVM optimization problem. Empirically, the Cutting-Plane Algorithm is several orders of magnitude faster than decomposition methods like SVM-Light for large datasets.
doi:10.1145/1150402.1150429 dblp:conf/kdd/Joachims06 fatcat:bdns3nypxbd4fmdmyi2c57kysu