Abstract
Case-based reasoning is deemed an important technology to alleviate the bottleneck of knowledge acquisition in Artificial Intelligence (AI). In case-based reasoning, knowledge is represented in the form of particular cases with an appropriate similarity measure rather than any form of rules. The case-based reasoning paradigm adopts the view that an Al system is dynamically changing during its life-cycle which immediately leads to learning considerations. Within the present paper, we investigate the problem of case-based learning of indexable classes of formal languages. Prior to learning considerations, we study the problem of case-based representability and show that every indexable class is case-based representable with respect to a fixed similarity measure. Next, we investigate several models of case-based learning and systematically analyze their strengths as well as their limitations. Finally, the general approach to case-based learnability of indexable classes of formal languages is prototypically applied to so-called containmet decision lists, since they seem particularly tailored to case-based knowledge processing.
| Original language | English |
|---|---|
| Pages (from-to) | 59-83 |
| Number of pages | 25 |
| Journal | New Generation Computing |
| Volume | 15 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 1997 Mar |
| Externally published | Yes |
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Keywords
- Case-based Learning
- Containment Decision Lists
- Formal Language Learning
- Inductive Inference
- Instance-based Learning
ASJC Scopus subject areas
- Theoretical Computer Science
- Software
- Hardware and Architecture
- Computer Networks and Communications
- Computational Theory and Mathematics
Cite this
On case-based learnability of languages. / Globig, Christoph; Jantke, Klaus P.; Lange, Steffen; Sakakibara, Yasubumi.
In: New Generation Computing, Vol. 15, No. 1, 03.1997, p. 59-83.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - On case-based learnability of languages
AU - Globig, Christoph
AU - Jantke, Klaus P.
AU - Lange, Steffen
AU - Sakakibara, Yasubumi
PY - 1997/3
Y1 - 1997/3
N2 - Case-based reasoning is deemed an important technology to alleviate the bottleneck of knowledge acquisition in Artificial Intelligence (AI). In case-based reasoning, knowledge is represented in the form of particular cases with an appropriate similarity measure rather than any form of rules. The case-based reasoning paradigm adopts the view that an Al system is dynamically changing during its life-cycle which immediately leads to learning considerations. Within the present paper, we investigate the problem of case-based learning of indexable classes of formal languages. Prior to learning considerations, we study the problem of case-based representability and show that every indexable class is case-based representable with respect to a fixed similarity measure. Next, we investigate several models of case-based learning and systematically analyze their strengths as well as their limitations. Finally, the general approach to case-based learnability of indexable classes of formal languages is prototypically applied to so-called containmet decision lists, since they seem particularly tailored to case-based knowledge processing.
AB - Case-based reasoning is deemed an important technology to alleviate the bottleneck of knowledge acquisition in Artificial Intelligence (AI). In case-based reasoning, knowledge is represented in the form of particular cases with an appropriate similarity measure rather than any form of rules. The case-based reasoning paradigm adopts the view that an Al system is dynamically changing during its life-cycle which immediately leads to learning considerations. Within the present paper, we investigate the problem of case-based learning of indexable classes of formal languages. Prior to learning considerations, we study the problem of case-based representability and show that every indexable class is case-based representable with respect to a fixed similarity measure. Next, we investigate several models of case-based learning and systematically analyze their strengths as well as their limitations. Finally, the general approach to case-based learnability of indexable classes of formal languages is prototypically applied to so-called containmet decision lists, since they seem particularly tailored to case-based knowledge processing.
KW - Case-based Learning
KW - Containment Decision Lists
KW - Formal Language Learning
KW - Inductive Inference
KW - Instance-based Learning
UR - http://www.scopus.com/inward/record.url?scp=0030736295&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0030736295&partnerID=8YFLogxK
U2 - 10.1007/BF03037560
DO - 10.1007/BF03037560
M3 - Article
AN - SCOPUS:0030736295
VL - 15
SP - 59
EP - 83
JO - New Generation Computing
JF - New Generation Computing
SN - 0288-3635
IS - 1
ER -