Name
Abstract | ||
|---|---|---|
Active learning, one of large and important branches in machine learning and data mining, aims to build an accurate learning model with a relatively small number of labeled points which are chosen actively by the constructed learning model. Active learning algorithms can play an important role when the cost of obtaining labels of data points is expensive. However, the active learning algorithms for the relevance vector machine regression, one of the famous kernel methods for the regression due to its sparseness, Bayesian characteristics, and kernel properties, have not been established yet, whilst those for other kernel methods are extensively developed. In this paper, we propose a transductive relevance vector machine to construct the active learning algorithm for relevance vector machine based a sparse Bayesian kernel regression model. The proposed method obtains its basis vectors from the unlabeled data set as well as the labeled one. Then, we also suggest three querying strategies which uses only the relevance vectors automatically selected by the developed model for active selection for data points to be labeled. Applied to several artificial and real data sets, the proposed method show significantly more accurate results than the benchmark, the random selection, while maintaining the sparse characteristic of the traditional relevance vector machines. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1016/j.ins.2016.09.017 | Inf. Sci. |
Keywords | Field | DocType |
Active learning,Sparse Bayesian regression,Transductive learning,Relevance vector | Online machine learning,Instance-based learning,Semi-supervised learning,Pattern recognition,Active learning (machine learning),Radial basis function kernel,Computer science,Polynomial kernel,Artificial intelligence,Relevance vector machine,Kernel method,Machine learning | Journal |
Volume | Issue | ISSN |
374 | C | 0020-0255 |
Citations | PageRank | References |
1 | 0.36 | 0 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Youngdoo Son | 1 | 10 | 3.17 |
| Jae Wook Lee | 2 | 33 | 8.37 |