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Exploiting Ensemble Diversity for Automatic Feature Extraction
| Content Provider | Semantic Scholar |
|---|---|
| Author | Brown, Gavin Yao, Xin Wyatt, Jeremy L. Wersing, Heiko Sendhoff, Bernhard |
| Copyright Year | 2002 |
| Abstract | We present an automatic method, based on a neural network ensemble, for extracting multiple, diverse and complementary sets of useful classification features from high dimensional data. We demonstrate the utility of these diverse representations for an image dataset, showing good classification accuracy and a high degree of dimensionality reduction. We then outline a number of possible extensions to the project in an evolutionary computation context. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://www.cs.bham.ac.uk/~gxb//research/iconippaper02.ps.gz |
| Alternate Webpage(s) | https://www.techfak.uni-bielefeld.de/~hwersing/BrownYaoWyattWersingSendhoff2002-EED.pdf |
| Alternate Webpage(s) | http://www.cs.bham.ac.uk/~nah/bibtex/papers/brownyao02exploiting.pdf |
| Alternate Webpage(s) | http://www.cs.man.ac.uk/~gbrown/publications/iconippaper02.pdf |
| Alternate Webpage(s) | http://www.techfak.uni-bielefeld.de/~hwersing/BrownYaoWyattWersingSendhoff2002-EED.pdf |
| Language | English |
| Access Restriction | Open |
| Subject Keyword | Artificial neural network Biological Neural Networks Dimensionality reduction Evolutionary computation Feature extraction Silo (dataset) |
| Content Type | Text |
| Resource Type | Article |
