G.J. Palmer and S.Craw. The selection of training cases for automated knowledge refinement. In Proceedings of the 4th European Symposium on the Validation and Verification of Knowledge Based Systems (EUROVAV97), pages 205--215, Leuven, Belgium, 1997. Home/Search Document Not in Database Summary Related Articles Check |
This paper is cited in the following contexts:
Sampling: An efficient, simple and robust technique for scaling.. - Addala (Correct)
....So geneticists carefully sample a relevant subset of genes and conduct lab studies on them and later extend these results to other genes. ffl Large databases does not always guarantee desirable results [18, 16] ffl Efficiency of knowledge discovery algorithms depend on the size of the data set [17]. ffl High cost of I O operations [15] ffl Dynamic nature of some large databases will affect discovered knowledge. For example, new data may be added from time to time and as a result, some existing knowledge would become invalid. The existing knowledge must be updated and the whole database ....
G.J. Palmer and S.Craw. The selection of training cases for automated knowledge refinement. In Proceedings of the 4th European Symposium on the Validation and Verification of Knowledge Based Systems (EUROVAV97), pages 205--215, Leuven, Belgium, 1997.
Knowledge Refinement to Debug and Maintain a Tablet.. - Craw, Boswell, Rowe (1997) Self-citation (Craw) (Correct)
....for the training case, in addition to the filler. This appears to be an advantage of refining a design system and is in contrast to experience with diagnostic systems, where one difficulty is selecting relevant training cases with which to filter effectively the many refined KBs generated [12]. Fault 2: incorrect quantity of binder Cases 1 and 52 are examples. Case 1 Input: Drug A, 10mg, 1 filler TFS 1A Output: Binder Gelatin 4.1 TFS 1B Output: Binder Gelatin 2.1 Case 52 Input: Drug G, 210mg, 2 fillers TFS 1A Output: Binder Maize Starch 4.0 TFS 1B Output: Binder Maize Starch 2.0 ....
.... so the field of KBS validation has seen various tools capable of generating test cases with a higher chance of finding faults; e.g. SYCOJET [1] We believe that refinement tools must make the best use of existing test cases as training data, but if necessary suggest new, complementary test cases [12]. 7. Conclusions We have found that our refinement techniques are equally applicable to design and diagnostic KBSs, since there is nothing fundamentally different about the knowledge content and problem solving, and so our knowledge skeleton and problem graph models apply. The exploratory nature ....
G. J. Palmer and S. Craw. The selection of training cases for automated knowledge refinement. In J. Vanthienen and F. van Harmelen, editors, Proceedings of the 4th European Symposium on the Validation and Verification of Knowledge Based Systems (EUROVAV97), pages 205--215, Leuven, Belgium, June 1997.
Online articles have much greater impact More about CiteSeer.IST Add search form to your site Submit documents Feedback
CiteSeer.IST - Copyright Penn State and NEC