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.

• Type: Conference
• Year: 2004
• Where: IDEAL
• Authors: Matthew C. Casey, Khurshid Ahmad
• Keywords: Classifier Systems, IDEAL 2004, In-situ Trained Systems, Multiple Classifier Systems