| dc.creator |
Resnick, Paul |
|
| dc.date |
2004-10-20T20:00:53Z |
|
| dc.date |
2004-10-20T20:00:53Z |
|
| dc.date |
1989-02-01 |
|
| dc.date.accessioned |
2013-10-09T02:47:11Z |
|
| dc.date.available |
2013-10-09T02:47:11Z |
|
| dc.date.issued |
2013-10-09 |
|
| dc.identifier |
AITR-1052 |
|
| dc.identifier |
http://hdl.handle.net/1721.1/6836 |
|
| dc.identifier.uri |
http://koha.mediu.edu.my:8181/xmlui/handle/1721 |
|
| dc.description |
This thesis explores ways to augment a model-based diagnostic program with a learning component, so that it speeds up as it solves problems. Several learning components are proposed, each exploiting a different kind of similarity between diagnostic examples. Through analysis and experiments, we explore the effect each learning component has on the performance of a model-based diagnostic program. We also analyze more abstractly the performance effects of Explanation-Based Generalization, a technology that is used in several of the proposed learning components. |
|
| dc.format |
101 p. |
|
| dc.format |
11635658 bytes |
|
| dc.format |
4564645 bytes |
|
| dc.format |
application/postscript |
|
| dc.format |
application/pdf |
|
| dc.language |
en_US |
|
| dc.relation |
AITR-1052 |
|
| dc.subject |
learning |
|
| dc.subject |
explanation-based learning |
|
| dc.subject |
model-basedstroubleshooting |
|
| dc.title |
Generalizing on Multiple Grounds: Performance Learning in Model-Based Technology |
|