FORWARD FEATURE SELECTION FOR SUPPORT VECTOR MACHINES

In one embodiment, the present invention includes a method for training a Support Vector Machine (SVM) on a subset of features (d′) of a feature set having (d) features of a plurality of training instances to obtain a weight per instance, approximating a quality for the d features of the feature set using the weight per instance, ranking the d features of the feature set based on the approximated quality, and selecting a subset (q) of the features of the feature set based on the ranked approximated quality. Other embodiments are described and claimed.