Luyao Teng

TL;DR: The experimental results demonstrate the feasibility and efficiency of the proposed collaborative and adaptive intrusion detection method and are shown to be more predominant than the methods that use a set of single type support vector machine U+0028 SVM U-0029 in terms of detection precision rate and recall rate.

TL;DR: A solution to the M-M assignment problem by improving the K-M algorithm with backtracking (KMB) is proposed and the proposed KMB algorithm is valid and the worst time complexity is O ( ( ? L a i ) 3 ) .

TL;DR: The orthogonal projection matrix and the weight coefficient vector are introduced to extend maximum mean discrepancy (MMD) so that it can minimize MMD and simultaneously eliminate the negative transfer.

TL;DR: A novel unsupervised feature extraction method, i.e., joint sparse representation and locality preserving projection (JSRLPP), in which the graph construction and feature extraction are simultaneously carried out, which adaptively learns the similarity matrix by sparse representation, and at the same time, learns the projection matrix by preserving local structure.

TL;DR: Experimental results show that the collaborative and adaptive intrusion detection method proposed in this paper is superior to the detection of the SVM in the detection accuracy and detection efficiency.