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IDEAL
2004
Springer

In-Situ Learning in Multi-net Systems

13 years 9 months ago
In-Situ Learning in Multi-net Systems
Abstract. Multiple classifier systems based on neural networks can give improved 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.
Matthew C. Casey, Khurshid Ahmad
Added 02 Jul 2010
Updated 02 Jul 2010
Type Conference
Year 2004
Where IDEAL
Authors Matthew C. Casey, Khurshid Ahmad
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