An adaptive support vector regression filter: A signal detection application

A new method for the construction of nonlinear adaptive filters called adaptive support vector regression is introduced for signal detection in noisy environments. A modification of support vector regression for online learning is motivated by the chunking approach and is based on repeated retraining of the filter parameters without the loss of former estimates. Performance of the proposed method was found superior to the method using a resource-allocating RBF network with Givens QR decomposition and pruning.