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Robust feature induction for support vector machines
2004
Twenty-first international conference on Machine learning - ICML '04
The goal of feature induction is to automatically create nonlinear combinations of existing features as additional input features to improve classification accuracy. Typically, nonlinear features are introduced into a support vector machine (SVM) through a nonlinear kernel function. One disadvantage of such an approach is that the feature space induced by a kernel function is usually of high dimension and therefore will substantially increase the chance of over-fitting the training data.
doi:10.1145/1015330.1015370
dblp:conf/icml/JinL04
fatcat:im6n7esugbcqtayxv7l7ni76oe