document: A sequential dual method for large scale multi-class linear svms Efficient training of direct multi-class formulations of linear Support Vector Machines is very useful in applications such as text classification with a huge number examples as well as features . This paper presents a fast dual method for this training . The main idea is to sequentially traverse through the training set and optimize the dual variables associated with one example at a time . The speed of training is enhanced further by shrinking and cooling heuristics . Experiments indicate that our method is much faster than state of the art solvers such as bundle , cutting plane and exponentiated gradient methods .
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