摘要
直推式支持向量机(TSVM)是支持向量机与直推式学习相结合的重要算法.文中为TSVM中的临时标签样本引入双模糊隶属度以及样本修剪策略,构建一种双模糊渐进直推式支持向量机(BFPTSVM)算法.该算法可有效降低TSVM的计算复杂度及核存储量.模拟实验表明该算法可取得比其他算法更好的分类性能,并且具有较快的收敛速度.
Transductive support vector machine learning into support vector machine. In (TSVM) is this paper, a well-known algorithm that integrates transductive a bi-fuzzy progressive transductive support vector machine (BFPTSVM) is constructed by introducing the bi-fuzzy memberships and sample-pruning strategy for the temporary labeled samples. BFPTSVM is capable of degrading the computational complexity and the store memory of TSVM. Simulation results show that BFPTSVM has better classification and convergence performance compared with other learning algorithms.
出处
《模式识别与人工智能》
EI
CSCD
北大核心
2009年第4期560-566,共7页
Pattern Recognition and Artificial Intelligence
基金
国家自然科学基金项目(No.30571059)
国家863计划项目(No.2006AA02Z190)资助