Quasi-objective Nonlinear Principal Component Analysis and applications to the atmosphere
NonLinear Principal Component Analysis (NLPCA) using three-hidden-layer feed-forward neural networks can produce solutions that over-fit the data and are non-unique. These problems have been dealt with by subjective methods during the network training. This study shows that these problems are intrin...
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| Format: | Others |
| Language: | en |
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University of British Columbia
2007
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| Online Access: | http://hdl.handle.net/2429/234 |