NDLI: A SVM active learning method based on confidence, KNN and diversity

Content Provider	IEEE Xplore Digital Library
Author	Yan Leng Xinyan Xu Chengli Sun Chuanfu Cheng Honglin Wan Jing Fang Dengwang Li
Copyright Year	2015
Description	Author affiliation: Shandong Coll. of Electron. Technol., Ji'nan, China (Xinyan Xu) \|\| Sch. of Inf., Nanchang Hangkong Univ., Nanchang, China (Chengli Sun) \|\| Coll. of Phys. & Electron., Shandong Normal Univ., Ji'nan, China (Yan Leng; Chuanfu Cheng; Honglin Wan; Jing Fang; Dengwang Li)
Abstract	Audio is an important part of multimedia, and it has many useful applications in real life. Audio event classification is a key technology in audio management and application. Supervised audio event classification requires labeling large amounts of samples, while manual labeling is a very time-consuming work. In this paper we propose $SVM_{CKNND},$ an active learning method for SVM classifier, to deal with the labeling problem in audio event classification. For $SVM_{CKNND},$ in each iteration, first, a low-confidence region is delimited; then based on KNN, the samples that are more likely to be on the true class boundary are taken as the informative ones; finally, redundancy that exists in the informative samples is reduced to further decrease manual labeling workload. Experimental results show that $SVM_{CKNND}$ performs better than another two SVM active learning algorithms, especially in classifying small-sample audio events.
Starting Page	1
Ending Page	6
File Size	248558
Page Count	6
File Format	PDF
ISBN	9781479970827
DOI	10.1109/ICME.2015.7177527
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-06-29
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Labeling Support vector machines Manuals Training Learning systems Speech Redundancy diversity Active learning SVM confidence KNN
Content Type	Text
Resource Type	Article

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