Name
Abstract | ||
|---|---|---|
A fielded machine learning system for vocational counselling is presented in which learning is based on adjustments of weights on links in a network. The system exemplifies how a specific representation, consisting of weights assigned to associations between keywords, enables the effective use of machine learning algorithms for acquiring and continually refining domain knowledge. The representation is designed for coping with the types of knowledge that can be found in weak theory domains, that is, knowledge too difficult to formalize because it is incomplete or vague. Knowledge acquisition and knowledge refinement processes are automated in order to efficiently decrease the effect of unreliable knowledge caused by unknown or unspecified biases inherent in the knowledge sources. The knowledge acquisition algorithms presented here are capable of coping with incomplete and vaguely defined domain knowledge. The knowledge refinement algorithms are used on-line to enable a continuous refinement of ill-defined domain knowledge. |
Year | DOI | Venue |
|---|---|---|
1994 | 10.1080/08839519408945458 | APPLIED ARTIFICIAL INTELLIGENCE |
Keywords | Field | DocType |
machine learning,domain knowledge | Procedural knowledge,Body of knowledge,Descriptive knowledge,Domain knowledge,Computer science,Knowledge-based systems,Knowledge extraction,Artificial intelligence,Knowledge base,Machine learning,Knowledge acquisition | Journal |
Volume | Issue | ISSN |
8 | 4 | 0883-9514 |
Citations | PageRank | References |
1 | 0.43 | 2 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Harald Kjellin | 1 | 4 | 4.28 |
| Magnus Boman | 2 | 227 | 49.08 |