Adaptive support vector machines for regression

Abstract

Support vector machines are a general formulation for machine learning. It has been shown to perform extremely well for a number of problems in classification and regression. However, in many difficult problems, the system dynamics may change with time and the resulting new information arriving incrementally will provide additional data. At present, there is limited work to cope with the computational demands of modeling time varying systems. Therefore, we develop the concept of adaptive support vector machines that can learn from incremental data. Results are provided to demonstrate the applicability of the adaptive support vector machines techniques for pattern classification and regression problems.