摘要
针对现有的主动学习算法在多分类器应用中存在准确率低、速度慢等问题,将基于仿射传播(AP)聚类的主动学习算法引入到多分类支持向量机中,每次迭代主动选择最有利于改善多类SVM分类器性能的N个新样本点添加到训练样本点中进行学习,使得在花费较小标注代价情况下,能够获得较高的分类性能。在多个不同数据集上的实验结果表明,新方法能够有效地减少分类器训练时所需的人工标注样本点的数量,并获得较高的准确率和较好的鲁棒性。
For the shortcomings of active learning algorithm existing in multi-class classifier application,such as low accuracy,slow speed and so on,this paper presented an improved active learning algorithm and its application to multi-class SVM.It presented a novel optimization method of training samples with affinity propagation（AP） clustering algorithm and active learning algorithm for multi-class SVM classification problem.This method choose the most beneficial N new samples added to the training samples for learning in order to spend less marked cost and get a good classification performance.Indicated in many different data set experimental result that,the proposed method gives large reduction in the number of human labeled samples to achieve similar classification accuracy,and has little computational overhead and good robustness
出处
《计算机应用研究》
CSCD
北大核心
2012年第9期3316-3319,共4页
Application Research of Computers
基金
国家"863"计划资助项目(2011AA010603)
关键词
仿射传播聚类
多分类支持向量机
主动学习算法
训练样本点优化
affinity propagation clustering
multi-class support vector machine （SVM）
active learning
training sample optimization
