基于信息反馈的半监督支持向量机算法

Semi-Supervised Support Vector Machine Algorithm Based on Information Feedback

针对直推式支持向量机中标记速度与标注精度之间的矛盾,提出一种信息反馈的半监督支持向量机算法,该算法利用上轮标注数量、重置次数、未标注边界样本数量等信息,动态调整标记样本数量,对区域标注和成对标注进行折衷,在继承渐进赋值和动态调整的同时,可以平衡标记速度与标记精度之间的矛盾,减少错误的传递和积累.在人工数据集和UCI数据集上的实验结果表明该算法在保证标注准确度的前提下提高算法速度.

In order to resolve the contradiction between the speed and the precision of transductive support vector machine, a semi-supervised vector machine algorithm based on information feedback is proposed. The algorithm uses the information of the number of last round, the number of reset, the number of unlabeled samples to adjust dynamically the number of labeled samples, and make a tradeoffbetween region labeling and pairwise tagging. While the progressive evaluating and dynamically adjusting, it can balance the contradiction between the marking speed and accuracy and reduces the transmission and accumulation of errors. The experimental results on AI data sets and UCI data sets show that the proposed algorithm can improve calculation speed on the premise of ensuring the accuracy of label precision.