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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Ramanathan, K. Sheng Uei Guan Iyer, L.R. |
| Copyright Year | 2006 |
| Description | Author affiliation: Dept. of Electr. & Comput. Eng., National Univ. of Singapore (Ramanathan, K.; Sheng Uei Guan; Iyer, L.R.) |
| Abstract | In supervised learning, most single solution neural networks such as constructive backpropagation give good results when used with some datasets but not with others. Others such as probabilistic neural networks (PNN) fit a curve to perfection but need to be manually tuned in the case of noisy data. Recursive percentage based hybrid pattern training (RPHP) overcomes this problem by recursively training subsets of the data, thereby using several neural networks. MultiLearner based recursive training (MLRT) is an extension of this approach, where a combination of existing and new learners are used and subsets are trained using the weak learner which is best suited for this subset. We observed that empirically, MLRT performs considerably well as compared to RPHP and other systems on benchmark data with 11% improvement in accuracy on the spam dataset and comparable performances on the vowel and the two-spiral problems |
| Sponsorship | IEEE Singapore Sect. IEEE Singapore SMC Chap. IEEE Singapore RA Chap |
| Starting Page | 1 |
| Ending Page | 5 |
| File Size | 204762 |
| Page Count | 5 |
| File Format | |
| ISBN | 1424400236 |
| DOI | 10.1109/ICCIS.2006.252267 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2006-06-07 |
| Publisher Place | Thailand |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Neural networks Backpropagation algorithms Supervised learning Neurons Clustering algorithms Genetics Testing Interpolation Machine learning Unsupervised learning Backpropagation Neural Networks Supervised Learning Recursive Percentage Based Hybrid Pattern Training (RPHP) Probabilistic Neural Networks (PNN) |
| Content Type | Text |
| Resource Type | Article |
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