Dan Luo* Pages 348 - 358 ( 11 )
Background: It presents a kind of multi-class image classification algorithm which is combined with Best Versus Second Best (BVSB) active learning technology and improved self-training semi-supervised learning technology.
Methods: The algorithm integrates the advantages of active learning; semi-supervised learning and extreme learning machine simultaneously. It has better performance than that of single algorithm when it is used in different sets of image target recognition.
Results: In addition, it also discussed the influence of various parameters on the algorithm performance in the experimental parts, and made related analysis of semi-supervised learning algorithm based on SVM (Support Vector Machine); the experimental results verified the superiority of proposed algorithm.
Improved active learning, semi-supervised learning algorithm, support vector machine, image classification method, Bayesian classifier, flexible neural tree.
School of Computer Science, Shaanxi Normal University, XiAn 710119