Online sparse least square support vector machines regression

Least square support vector machines regression without sparsity needs longer training time currently,and is not adapted to online real-time training.A better method of online sparse least square support vector machines regression(SVMR) is proposed.Less training time is needed by using sample dictionary.The online SVMR whose samples is available one by one by adding the samples sequentially is convergence theoretically.The simulation result shows that the algorithm has a better sparsity and real-time performance and could be applied in modeling and real-time control etc.