CAJ | 학술논문

针对支持向量机（souport vector machine，SVM）训练学习过程中样本分布不均衡、难以获得大量带有类标注样本的问题，提出一种基于委员会投票选择（query by committee，QBC）的 SVM 主动学习算法 QBC-AS-VM，将改进的 QBC 主动学习方法与加权 SVM 方法有机地结合应用于 SVM 训练学习中，通过改进的 QBC 主动学习，主动选择那些对当前 SVM 分类器最有价值的样本进行标注，在 SVM 主动学习中应用改进的加权 SVM，减少了样本分布不均衡对 SVM 主动学习性能的影响，实验结果表明在保证不影响分类精度的情况下，所提出的算法需要标记的样本数量大大少于随机采样法需要标记的样本数量，降低了学习的样本标记代价，提高了 SVM 泛化性能而且训练速度同样有所提高。
침대지지향량궤（souport vector machine，SVM）훈련학습과정중양본분포불균형、난이획득대량대유류표주양본적문제，제출일충기우위원회투표선택（query by committee，QBC）적 SVM 주동학습산법 QBC-AS-VM，장개진적 QBC 주동학습방법여가권 SVM 방법유궤지결합응용우 SVM 훈련학습중，통과개진적 QBC 주동학습，주동선택나사대당전 SVM 분류기최유개치적양본진행표주，재 SVM 주동학습중응용개진적가권 SVM，감소료양본분포불균형대 SVM 주동학습성능적영향，실험결과표명재보증불영향분류정도적정황하，소제출적산법수요표기적양본수량대대소우수궤채양법수요표기적양본수량，강저료학습적양본표기대개，제고료 SVM 범화성능이차훈련속도동양유소제고。
To the problem that large-scale labeled samples is not easy to acquire and the class-unbalanced dataset in the course of souport vector machine (SVM)training,an active learning algorithm based on query by committee (QBC)for SVM(QBC-ASVM)is proposed,which efficiently combines the improved QBC active learning and the weighted SVM.In this method,QBC active learning is used to select the samples which are the most valuable to the current SVM classifier,and the weighted SVM is used to reduce the impact of the unba-lanced data set on SVMs active learning.The experimental results show that the proposed approach can consid-erably reduce the labeled samples and costs compared with the passive SVM,and at the same time,it can ensure that the accurate classification performance is kept as the passive SVM,and the proposed method improves gen-eralization performance and also expedites the SVM training.