Ruixiang Li1,2, Yingying Liu3, Yaqiong Qiao1,2, Te Ma1,2, Bo Wang4, Xiangyang Luo1,2,*
CMC-Computers, Materials & Continua, Vol.59, No.2, pp. 591-606, 2019, DOI:10.32604/cmc.2019.05208
Abstract High-density street-level reliable landmarks are one of the important foundations for street-level geolocation. However, the existing methods cannot obtain enough street-level landmarks in a short period of time. In this paper, a street-level landmarks acquisition method based on SVM (Support Vector Machine) classifiers is proposed. Firstly, the port detection results of IPs with known services are vectorized, and the vectorization results are used as an input of the SVM training. Then, the kernel function and penalty factor are adjusted for SVM classifiers training, and the optimal SVM classifiers are obtained. After that, the classifier sequence More >
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