In-situ learning in multi-net systems
Casey, M, Ahmad, K, Yang, ZR, Everson, R and Yin, H (2004) In-situ learning in multi-net systems In: 5th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL 2004), 2004-08-25 - 2004-08-27, Execter, ENGLAND.
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Multiple classifier systems based on neural networks can give proved generalisation performance as compared with single classifier systems. We examine collaboration in multi-net systems through in-situ learning exploring how generalisation can be improved through the simultaneous learning in networks and their combination. We present two in-situ trained systems; first, one based upon the simple ensemble, combining supervised networks in parallel, and second, a combination of unsupervised and supervised networks in, sequence. Results for these are compared with existing approaches demonstrating that in-situ trained systems perform better than similar pre-trained systems.
|Item Type:||Conference or Workshop Item (UNSPECIFIED)|
|Uncontrolled Keywords:||Science & Technology, Technology, Computer Science, Artificial Intelligence, Computer Science, Theory & Methods, Computer Science, CLASSIFIERS|
|Divisions:||Faculty of Engineering and Physical Sciences > Computing Science|
|Depositing User:||Symplectic Elements|
|Date Deposited:||17 Jun 2011 16:03|
|Last Modified:||23 Sep 2013 18:42|
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