Learning rates of support vector machine classifier for density level detection ?
In this paper, we consider the learning rates of support vector machines (SVMs) classifier for density level detection (DLD) problem. Using an established classification framework, we get error decomposition which consists of regularization error and sample error. Based on the decomposition, we obtain learning rates of SVMs classifier for DLD under some assumptions.
learning rates support vector machines density level detection anomaly detection