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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Chen hua Zhang xiao-gang Zhang Jing Ding Li-hua |
| Copyright Year | 2009 |
| Abstract | A proportion factor is constructed though the Maximum Aposteriori Probability of examples in test data to select the training examples in incremental learning process. Instead of complex normal classify loss expression, the proportion factor λ is used to estimate the classify loss to improve classification efficiency. The final experiment shows that this algorithm is feasible, and more accurate than simple Bayesian classifier. The computing time is highly reduced on the optimal selection of examples in incremental learning process. |
| Starting Page | 126 |
| Ending Page | 128 |
| File Size | 345864 |
| Page Count | 3 |
| File Format | |
| ISBN | 9780769535074 |
| DOI | 10.1109/CSIE.2009.305 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2009-03-31 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Maximum a posteriori estimation Algorithm design and analysis Data engineering Probability distribution Data mining Bayesian classifier Computer science Bayesian methods incremental learning simplified algorithm Training data Machine learning Testing |
| Content Type | Text |
| Resource Type | Article |
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