Fast and Scalable Local Kernel Machines

A computationally efficient approach to local learning with kernel methods is presented. The Fast Local Kernel Support Vector Machine (FaLK-SVM) trains a set of local SVMs on redundant neighbourhoods in the training set and an appropriate model for each query point is selected at testing time according to a proximity strategy. Supported by a recent result by Zakai and Ritov (2009) relating consistency and localizability, our approach achieves high classification accuracies by dividing the separation function in local optimisation problems that can be handled very efficiently from the

• Type: Journal
• Year: 2010
• Where: JMLR
• Authors: Nicola Segata, Enrico Blanzieri
• Keywords: Fast Local Kernel, JMLR 2010, Local Optimisation Problems, Support Vector Machine