A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf
.
Training linear SVMs in linear time
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
doi:10.1145/1150402.1150429
dblp:conf/kdd/Joachims06
fatcat:bdns3nypxbd4fmdmyi2c57kysu