Name
Abstract | ||
|---|---|---|
Support vector machine (SVM) based active learning technique has played 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 scheme that combines semi-supervised learning, ensemble learning and active learning in a uniform framework. Concretely, unlabeled data is exploited to facilitate ensemble learning by helping augment the diversity among the base SVM classifiers, and then the learned SVM ensemble model is used to identify the most informative examples for active learning. In particular, a bias-weighting mechanism is developed to guide the ensemble 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 | 10.1145/1816041.1816070 | CIVR |
Keywords | Field | DocType |
small example problem,svm-based active learning algorithm,svm active learning,base svm classifier,novel scheme,active learning,proposed scheme,svm ensemble model,asymmetric distribution problem,ensemble model,active learning technique,empirical study,image retrieval,semi supervised learning,ensemble learning,boosting,support vector machine | Online machine learning,Semi-supervised learning,Instance-based learning,Active learning,Active learning (machine learning),Computer science,Unsupervised learning,Artificial intelligence,Boosting (machine learning),Ensemble learning,Machine learning | Conference |
Citations | PageRank | References |
3 | 0.39 | 6 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jun Wu | 1 | 125 | 15.66 |
| Zheng-Kui Lin | 2 | 7 | 1.45 |
| Ming-Yu Lu | 3 | 102 | 10.00 |