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Analysis of SVM with Indefinite Kernels
Neural Information Processing Systems
The recent introduction of indefinite SVM by Luss and d'Aspremont  has effectively demonstrated SVM classification with a non-positive semi-definite kernel (indefinite kernel). This paper studies the properties of the objective function introduced there. In particular, we show that the objective function is continuously differentiable and its gradient can be explicitly computed. Indeed, we further show that its gradient is Lipschitz continuous. The main idea behind our analysis is that thedblp:conf/nips/YingCG09 fatcat:kjenmr5elfhe3mcgj4fmi5rvce