Feature Selection Using Linear Support Vector Machines

In this study we propose a method for feature selection and iterative classifier training based on Support Vector Machines (SVM) with linear kernels. We explore how this and other feature selection methods can be used to make tradeoffs between the amount of training data and the sparsity of the document representation for the fixed amount of system memory.