摘要
针对半监督学习中渐进直推支持向量机(PTSVM)算法每次标注的样本数太少、训练速度慢、回溯式学习多、学习性能不稳定的问题,提出一种快速的渐进直推支持向量机学习算法.该算法利用支持向量的信息,基于支持向量域描述(SVDD)选择新标注、无标签的样本点,以区域标注法代替 PTSVM 的成对标注法,不仅继承了其渐进赋值和动态调整的规则,而且在保持甚至提高算法精度的同时,大大提高算法速度.在人工模拟数据和真实数据上的实验结果表明该算法的有效性.
In semi-supervised learning, progressive transductive support vector machine （PTSVM） has some drawbacks, such as few sample labeled in each iteration, low training speed, many backtrack learning steps, and unstable learning performance. Aiming at these problems, a fast progressive transductive support vector machines learning algorithm is proposed. It selects new unlabeled samples based on support vector domain description （SVDD） by using the information of support vectors. Using region labeling rule instead of pairwise labeling rule of PTSVM, the algorithm inherits progressive labeling and dynamic adjusting of the PTSVM. And meanwhile it increases the computational speed and keeps even improves the accuracy. Experimental results on synthetic and real datasets show the validity of the proposed algorithm.
出处
《模式识别与人工智能》
EI
CSCD
北大核心
2008年第6期721-727,共7页
Pattern Recognition and Artificial Intelligence
基金
国家自然科学基金资助项目(No.60574075,60703118)
关键词
半监督学习
支持向量机
直推式学习
支持向量域描述(SVDD)
Semi-Supervised Learning, Support Vector Machines, Transductive Learning, Support Vector Domain Description （SVDD）
