Kernel Classification via Integrated Squared Error
JooSeuk Kim, Clayton D. Scott · 2007 IEEE/SP 14th Workshop on Statistical Signal Processing · 2007
Nonparametric kernel methods are widely used and proven to be successful in many statistical learning problems. Wellknown examples include the kernel density estimate (KDE) for density estimation and the support vector machine (SVM) for classification. We propose a kernel classifier that optimizes an integrated squared error (ISE) criterion based on a "difference of densities" formulation. Our classifier is sparse, like SVMs, and performs comparably to state-of-the-art kernel methods. Furthermore, and unlike SVMs, the ISE criterion does not require the user to set any unknown regularization parameters. As a consequence, classifier training is faster than for support vector methods.