Name
Abstract | ||
|---|---|---|
In this paper, we propose a novel sparse online co-regularization framework for multiview semi-supervised learning, which concerns using a small portion of the arrived training examples to represent predictors during the online learning process. This framework makes use of Fenchel conjugates to perform sparse online co-regularization process in the dual function. The use of tolerance function enforces sparsity. Detailed experiments on artificial and real world data sets verify the utility of our approaches. This paper paves a way to the design and analysis of sparse online co-regularization algorithms. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/ICPR.2014.630 | ICPR |
Keywords | Field | DocType |
&,sparse online co-regularization, multiview semi-supervised learning, &epsi,dual function,tolerance,learning (artificial intelligence),sparse online co-regularization,conjugate functions,online learning process,multiview semisupervised learning,tolerance function,sparse online coregularization framework,epsi,multiview semi-supervised learning,tolerance, fenchel conjugates,fenchel conjugates,prediction algorithms,vectors,algorithm design and analysis,kernel | Kernel (linear algebra),Online learning,Data set,Algorithm design,Pattern recognition,Computer science,Sparse approximation,Prediction algorithms,Regularization (mathematics),Artificial intelligence,Conjugate functions,Machine learning | Conference |
ISSN | Citations | PageRank |
1051-4651 | 0 | 0.34 |
References | Authors | |
10 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Boliang Sun | 1 | 0 | 0.34 |
| Min Tang | 2 | 623 | 51.33 |
| Li Guohui | 3 | 447 | 76.53 |