文摘
Propose an incremental online LS-SVMs learning algorithm to incorporate the support vectors chunk-by-chunk. Employ block Gaussian elimination method to dynamically update the LS-SVMs model. Theoretically, analyze the computational complexity of the proposed algorithm, which is demonstrated to be much lower than the state-of-the-arts. Experimental results on benchmark and real-world datasets show the validity and efficiency of the proposed algorithm.