Self-Organizing Maps
The Adaption Knowledge Bottleneck: How to Ease it by Learning from Cases
ICCBR '97 Proceedings of the Second International Conference on Case-Based Reasoning Research and Development
Techniques and Knowledge Used for Adaptation During Case-Based Problem Solving
IEA/AIE '98 Proceedings of the 11th International Conference on Industrial and Engineering Applications of Artificial In telligence and Expert Systems: Tasks and Methods in Applied Artificial Intelligence
Incorporating AI into Military Decision Making: An Experiment
IEEE Intelligent Systems
Expanding self-organizing map for data visualization and cluster analysis
Information Sciences: an International Journal - Special issue: Soft computing data mining
Using case-base data to learn adaptation knowledge for design
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Acquiring case adaptation knowledge: a hybrid approach
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
A new model of self-organizing neural networks and its application in data projection
IEEE Transactions on Neural Networks
ViSOM - a novel method for multivariate data projection and structure visualization
IEEE Transactions on Neural Networks
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Case-Based Reasoning (CBR) is a methodology that reuses the solutions of previous similar problem to solve new problems. Adaptation is one of the most difficult parts of CBR cycle, especially, when the solution space with multi-dimension. This paper discusses the adaptation of high dimensional solution space and proposes a possible approach for it. Visualisation induced Self Organising Map (ViSOM) is used to map the problem space and solution space first, then a BackPropagation (BP) network is applied to analyse the relations between these two maps. A simple military scenario is used as case study for evaluation.