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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Genet, M.G. Petrowski, A. |
| Copyright Year | 2003 |
| Description | Author affiliation: GET/INT, Evry, France (Genet, M.G.; Petrowski, A.) |
| Abstract | This paper describes a constructive learning system for conditional probability distribution estimations. First, the system carries out an unsupervised partitioning of the input space into small regions containing input vectors of the training set. It then computes coarse estimates of the output value conditional probability distribution, knowing the region of the corresponding inputs. Finally, a supervised region-gathering occurs when the output probability distribution, which are associated to neighboring regions, are not significantly different. This is done by using statistical tests. The region gathering stage allows the refinement of the estimates while reducing the complexity of the system structure. A neural discrimination tree implementation is presented and applied to sunspot number prediction. It is fast, parameter free, easy to use and provides excellent results. |
| Sponsorship | Int. Neural Network Soc. IEEE Neural Networks Soc |
| Starting Page | 814 |
| Ending Page | 819 |
| File Size | 447062 |
| Page Count | 6 |
| File Format | |
| ISBN | 0780378989 |
| ISSN | 10987576 |
| DOI | 10.1109/IJCNN.2003.1223487 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2003-07-20 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Probability distribution Learning systems Distributed computing Testing Supervised learning Adaptive systems Stochastic systems Stochastic processes Random variables Multi-layer neural network |
| Content Type | Text |
| Resource Type | Article |
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