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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Sugiyama, M. Ogawa, H. |
| Copyright Year | 2002 |
| Description | Author affiliation: Dept. of Comput. Sci., Tokyo Inst. of Technol., Japan (Sugiyama, M.; Ogawa, H.) |
| Abstract | In supervised learning, the selection of sample points and models is crucial for acquiring a higher level of the generalization capability. So far, the problems of active learning and model selection have been independently studied. If sample points and models are simultaneously optimized, then a higher level of the generalization capability is expected. We call this problem active learning with model selection. However, this problem can not be generally solved by simply combining existing active learning and model selection techniques because of the active learning/model selection dilemma: the model should be fixed for selecting sample points, and conversely the sample points should be fixed for selecting models. In spite of the dilemma, we show that the problem of active learning with model selection can be straightforwardly solved if there is a set of sample points that is optimal for all models in consideration. Based on the idea, we give a procedure for active learning with model selection in trigonometric polynomial models. |
| Sponsorship | IEEE IEEE Neural Networks Soc. (NNS) Int. Neural Network Soc |
| Starting Page | 2917 |
| Ending Page | 2922 |
| File Size | 576536 |
| Page Count | 6 |
| File Format | |
| ISBN | 0780372786 |
| ISSN | 10987576 |
| DOI | 10.1109/IJCNN.2002.1007612 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2002-05-12 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Supervised learning Diversity reception Polynomials Learning systems Computer science World Wide Web Degradation Additive noise Error correction Optimal control |
| Content Type | Text |
| Resource Type | Article |
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