260 likes | 417 Views
excerpt. CBR methods The Data-- Knowledge Dimension. Data intensive - Knowledge poor - A case is a data record - Similarity asessment based on simple metric Knowledge intensive - Data Poor - A case is a user experience - Similarity asessment is an explanation process
E N D
excerpt ICML-99 (A. Aamodt)
CBR methods The Data-- Knowledge Dimension • Data intensive - Knowledge poor • - A case is a data record - Similarity asessment based on simple metric • Knowledge intensive - Data Poor - A case is a user experience - Similarity asessment is an explanation process • Both knowledge and data intensive - Multiple case contents - Multiple similarity asessment methods ICML-99 (A. Aamodt)
Dynamic Memory (Scank & Kolodner 83) ICML-99 (A. Aamodt)
Example ICML-99 (A. Aamodt)
Category Structure (Porter & Bareiss 87) ICML-99 (A. Aamodt)
t h i n g g e n e r i c c o n c e p t s g d o m a i n c o n c e p t s c a s e s c a s e c a s e c a s e 0 3 9 7 6 1 1 2 CreekL Knowledge Types l e n e r a ICML-99 (A. Aamodt)
Integrated approaches • Case-based and inductive learning - CBR & Data Mining • CBR and decision trees - Example: INRECA (Esprit III) • CBR and Bayesian networks - Example: NOEMIE (Esprit IV) ICML-99 (A. Aamodt)