๐”– Bobbio Scriptorium
โœฆ   LIBER   โœฆ

Learning adaptation knowledge to improve case-based reasoning

โœ Scribed by Susan Craw; Nirmalie Wiratunga; Ray C. Rowe


Publisher
Elsevier Science
Year
2006
Tongue
English
Weight
1007 KB
Volume
170
Category
Article
ISSN
0004-3702

No coin nor oath required. For personal study only.


๐Ÿ“œ SIMILAR VOLUMES


Adaptation rule learning for case-based
โœ Huan Li; Xin Li; Dawei Hu; Tianyong Hao; Liu Wenyin; Xiaoping Chen ๐Ÿ“‚ Article ๐Ÿ“… 2009 ๐Ÿ› John Wiley and Sons ๐ŸŒ English โš– 284 KB
Case-based reasoning and improved adapti
โœ Andreas Schirmer ๐Ÿ“‚ Article ๐Ÿ“… 2000 ๐Ÿ› John Wiley and Sons ๐ŸŒ English โš– 699 KB

Most scheduling problems are notoriously intractable, so the majority of algorithms for them are heuristic in nature. Priority rule-based methods still constitute the most important class of these heuristics. Of these, in turn, parametrized biased random sampling methods have attracted particular in

Learning assistance mechanism using case
โœ Ban Kaku; Tetsuya Matsumoto; Norimichi Kojo; Guo Xin ๐Ÿ“‚ Article ๐Ÿ“… 1998 ๐Ÿ› John Wiley and Sons ๐ŸŒ English โš– 245 KB

An intelligent learning system can provide users with different learning materials and questions of fixed difficulty according to the learners skill and knowledge. However, even the same question might have a different difficulty for different learners or at different learning stages. In this paper,

A context model for knowledge-intensive
โœ PINAR ร–ZTรœRK; AGNAR AAMODT ๐Ÿ“‚ Article ๐Ÿ“… 1998 ๐Ÿ› Elsevier Science ๐ŸŒ English โš– 436 KB

Decision-support systems that help solving problems in open and weak theory domains, i.e. hard problems, need improved methods to ground their models in real-world situations. Models that attempt to capture domain knowledge in terms of, e.g. rules or deeper relational networks, tend either to become