- Title
- Generalisation with symbolic knowledge in online classification
- Creator
- Kang, Byeongho; Dazeley, Richard
- Date
- 2008
- Type
- Text; Conference paper
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/40659
- Identifier
- vital:5827
- Abstract
- Increasingly, researchers and developers of knowledge based systems (KBS) have been incorporating the notion of context. For instance, Repertory Grids, Formal Concept Analysis (FCA) and Ripple-Down Rules (RDR) all integrate either implicit or explicit contextual information. However, these methodologies treat context as a static entity, neglecting many connectionists’ work in learning hidden and dynamic contexts, which aid their ability to generalize. This paper presents a method that models hidden context within a symbolic domain in order to achieve a level of generalisation. The method developed builds on the already established Multiple Classification Ripple-Down Rules (MCRDR) approach and is referred to as Rated MCRDR (RM). RM retains a symbolic core, while using a connection based approach to learn a deeper understanding of the captured knowledge. This method is applied to a number of online classification environments and results indicate that the method can learn the information that experts have difficulty providing.
- Publisher
- Hanoi, Vietnam Pacific Rim International conferences on Artificial Intelligence (PRICAI), and School of Computing, University of Tasmania
- Relation
- PKAW-08: Proceedings of the Pacific Rim Knowledge Acquisition Workshop 2008
- Rights
- Unknown copyright
- Rights
- This metadata is freely available under a CCO license
- Subject
- Hidden context; Knowledge based systems; Knowledge representation; Ripple-down rules; Situation cognition
- Reviewed
- Hits: 632
- Visitors: 602
- Downloads: 3
| Thumbnail | File | Description | Size | Format |
|---|