— We discuss sparse support vector machines (sparse SVMs) trained in the reduced empirical feature space. Namely, we select the linearly independent training data by the Cholesky...
Abstract. Practical pattern classification and knowledge discovery problems require selecting a useful subset of features from a much larger set to represent the patterns to be cl...
In this work, we suggest a new feature selection technique that lets us use the wrapper approach for finding a well suited feature set for distinguishing experiment classes in hig...
Recently, feature maps have been applied to various problem domains.The success of some of these applications critically depends on whether feature maps are topologically ordered. ...
A frequent problem in density level-set estimation is the choice of the right features that give rise to compact and concise representations of the observed data. We present an e...