document: Training linear SVMs in linear time Linear Support Vector Machines ( SVMs ) have become one of the most prominent machine learning techniques for high-dimensional 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 0 ( s , n ) 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 .
doc_bio_tags: O O O O O O O B I I O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O