| 摘要 |
With the advent of big-data age, it is essential to organize, analyze, retrieve and protect the
useful data or sensitive information in a fast and efficient way for customers from different industries
and fields. Least squares support vector machine (LS-SVM) has an outstanding advantage of lower
computational complexity than that of standard support vector machines. Its shortcomings are the loss
of sparseness and robustness. Thus it usually results in slow testing speed and poor generalization
performance. In this paper, a least squares support vector machine with L1 penalty (L1-LS-SVM) is
proposed to deal with above shortcomings. A minimum of 1-norm based object function is chosen to
get the sparse and robust solution based on the idea of basis pursuit (BP) in the whole feasibility
region. A real Chinese corpus from Fudan University is used to demonstrate the effectiveness of this
model. The experimental results show that L1-LS-SVMcan obtain a small number of support vectors
and improve the generalization ability of LS-SVM. |