Year: 2020
Type: article
Abstract: In this paper we introduce the idea of two-stage learning for multiple kernel SVM (MKSVM) and present a new MKSVM algorithm based on two-stage learning (MKSVM-TSL). The first stage is the pre-learning and its aim is to obtain the information of data such that the “important” samples for classification can be generated in the formal learning stage and these samples are uniformly ergodic Markov chain (u.e.M.c.). To study comprehensively the proposed MKSVM-TSL algorithm, we estimate the generalization bound of MKSVM based on u.e.M.c. samples and obtain its fast learning rate. And in order to show the performance of the proposed MKSVM-TSL algorithm for better, we also perform the numerical experiments on various publicly available datasets. From the experimental results, we can find that compared to three classical multiple kernel learning (MKL) algorithms, the proposed MKSVM-TSL algorithm has better performance in three aspects of the total time of sampling and training, the accuracy and the sparsity of classifiers, respectively.
Source: IEEE Access
Authors: Xingrui Gong, Bin Zou, Yuze Duan, Jie Xu, Qingxin Luo, Yan Yang
Institutions: Hubei University, Wuhan Qingchuan University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,750
Awards: G718321269, G7570466231
OpenAlex ID: W3033342863