Paper Info
Title
Enhancing SVM Active Learning for Image Retrieval Using Semi-supervised Bias-Ensemble
Abstract
Support vector machine (SVM) based active learning technique plays a key role to alleviate the burden of labeling in relevance feedback. However, most SVM-based active learning algorithms are challenged by the small example problem and the asymmetric distribution problem. This paper proposes a novel active learning scheme that deals with SVM ensemble under the semi-supervised setting to address the fist problem. For the second problem, a bias-ensemble mechanism is developed to guide the classification model to pay more attention on the positive examples than the negative ones. An empirical study shows that the proposed scheme is significantly more effective than some existing approaches.
Year
DOI
Venue
2010
10.1109/ICPR.2010.777
ICPR
Keywords
Field
DocType
learning (artificial intelligence),small example problem,semi-supervised bias-ensemble,active learning scheme,enhancing svm,semi-supervised learning,svm ensemble,bias-ensemble mechanism,active learning,svm-based active learning algorithm,proposed scheme,image retrieval,asymmetric distribution problem,classification model,active learning technique,ensemble learning,relevance feedback,novel active learning scheme,fist problem,content-based retrieval,support vector machines,support vector machine,empirical study,semi supervised learning,learning artificial intelligence,classification algorithms,supervised learning,radio frequency
Active learning,Semi-supervised learning,Pattern recognition,Ranking SVM,Active learning (machine learning),Computer science,Support vector machine,Supervised learning,Artificial intelligence,Statistical classification,Ensemble learning,Machine learning
Conference
ISSN
ISBN
Citations 
1051-4651
978-1-4244-7542-1
5
PageRank 
References 
Authors
0.43
4
3
Name
Order
Citations
PageRank
Jun Wu112515.66
Ming-Yu Lu210210.00
Chun-Li Wang350.43