Name
Abstract | ||
|---|---|---|
We present a rough set approach to vague concept approximation within the adaptive learning framework. In particular, the role of extensions of approximation spaces in searching for concept approximation is emphasized. Boundary regions of approximated concepts within the adaptive learning framework are satisfying the higher order vagueness condition, i.e., the boundary regions of vague concepts are not crisp. There are important consequences of the presented framework for research on adaptive approximation of vague concepts and reasoning about approximated concepts. An illustrative example is included showing the application of Boolean reasoning in adaptive learning. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1007/11548669_4 | RSFDGrC (1) |
Keywords | Field | DocType |
boundary region,higher order vagueness condition,approximated concept,adaptive learning,adaptive approximation,rough set,boolean reasoning,vague concept approximation,concept approximation,approximation space,vague concept,satisfiability,higher order,rough sets | Ordered set,Discrete mathematics,Vagueness,Computer science,Adaptive method,Concept learning,Rough set,Boolean algebra,Artificial intelligence,Boolean reasoning,Adaptive learning,Machine learning | Conference |
Volume | ISSN | ISBN |
3641 | 0302-9743 | 3-540-28653-5 |
Citations | PageRank | References |
8 | 0.94 | 8 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Andrzej Skowron | 1 | 5062 | 421.31 |
| Roman Swiniarski | 2 | 154 | 5.96 |