By Hector Munoz-Avila, Francesco Ricci
This publication constitutes the refereed complaints of the sixth overseas convention on Case-Based Reasoning, ICCBR 2005, held in Chicago, IL, united states, in August 2005.
The 19 revised complete examine papers and 26 revised poster papers offered including the abstracts of three invited talks have been rigorously reviewed and chosen from seventy four submissions. The papers handle all present foundational, theoretical and examine facets of case-based reasoning in addition to complex purposes both with leading edge advertisement deployment or useful, social, environmental or monetary significance.
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Additional info for Case-Based Reasoning Research and Development: 6th International Conference on Case-Based Reasoning, ICCBR 2005, Chicago, IL, USA, August 23-26, 2005,
1998). CBR for dynamic situation assessment in an agent-oriented setting. W. J. ), Case-Based Reasoning Integrations: Papers from the AAAI Workshop (Technical Report WS-98-15). Madison, WI: AAAI Press. ca Abstract. It has been shown that an ensemble of classiﬁers increases the accuracy compared to the member classiﬁers provided they are diverse. One way to produce this diversity is to base the classiﬁers on diﬀerent case-bases. In this paper, we propose the mixture of experts for casebased reasoning (MOE4CBR), where clustering techniques are applied to cluster the case-base into k groups, and each cluster is used as a case-base for our k CBR classiﬁers.
Minimizes the sum of squared distance from the mean within each cluster. Hierarchical clustering proceeds successively by either merging smaller clusters into larger ones (agglomerative approach), or by splitting larger clusters (divisive approach). 24 N. Arshadi and I. Jurisica Clustering and feature selection techniques have been applied to CBR systems as well. Yang and Wu  propose a method that groups the case-base into smaller case-bases, and then each case-base is maintained individually.
The two widely used clustering approaches in (ultra) high-dimensional DNA microarrays [22, 23] are k-means clustering  and 26 N. Arshadi and I. Jurisica P r(y|x) Gating Network g1 = P r(C1 |x) Network Expert 1 gk = P r(Ck |x) Network Expert 2 P r(y|x, C1 ) Network Expert k P r(y|x, Ck ) Fig. 1. Mixture of Experts: Terminal nodes are experts, and the non-terminal node is the gating network. The gating network returns the probability that the query x belongs to class Y Cluster 1 Case−Base 1 TA3 Cluster 2 P r(C1 |x) Case−Base 2 TA3 Training Set (Case−Base) Cluster k Case-Base k TA3 Gating Network P r(y|x) P r(Ck |x) Fig.