期刊
KNOWLEDGE AND INFORMATION SYSTEMS
卷 23, 期 3, 页码 345-372出版社
SPRINGER LONDON LTD
DOI: 10.1007/s10115-009-0220-4
关键词
Semi-supervised classification; Tri-training; Extreme learning machine; Incremental learning; Reversible derivation
资金
- National Nature Science Foundation of China [60674073]
- National Major Technology R&D Program of China [2006BAB14B05]
- National Basic Research Program of China [2006CB403405]
- National High Technology Research and Development Program of P. R. China [2007AA04Z158]
Tri-training method proposed by Zhou et al., is an excellent method for semi-supervised classification; nevertheless, the heavy computational burden caused by the retraining strategy prevents the further application of tri-training method. To address this problem, this paper proposes the ternary reversible extreme learning machines (TRELM) which is an incremental tri-training method without relying on the retraining strategy. TRELM employs three reversible extreme learning machines (RELM) as its base learners and trains the RELM with extended (or detected) samples in each learning round. RELM is an incremental learning method with reversible derivation capability. RELM can overcome the difficulty for most incremental learning methods in removing the influence of previously learned mistaken samples. Experimental results indicate that TRELM significantly improves the learning speed of tri-training method. In addition, TRELM achieves comparable (or even better) classification performance to other effective semi-supervised learning methods. TRELM is an appropriate choice for semi-supervised classification tasks with large amounts of data sets or with strict demands for learning speed and classification accuracy.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据