Engineering
Engineering
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(1999) Corkish, Richard; Altermatt , Pietro P.; Heiser, GernotConference Paper
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(1999) Schumacher, J; Altermatt, Pietro P.; Heiser, Gernot; Aberle, Armin G.Conference Paper
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(1999) Deller, L; Heiser, GernotConference PaperLinking and loading are the final steps in preparing a program for execution. This paper assesses issues concerning dynamic and static linking in traditional as well as single-address-space operating systems (SASOS). Related loading issues are also addressed. We present the dynamic linking model implemented in the Mungi SASOS and discuss its strengths and limitations. Benchmarking shows that dynamic linking in Mungi carries less overhead than dynamic linking in SGI`s Irix operating system
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(1999) Schumacher, J; Altermatt, Peter; Heiser, Gernot; Aberle, ArminConference Paper
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(1999) Altermatt, Peter; Sinton, Ron; Heiser, GernotConference Paper
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(1999) Altermatt, Peter; Heiser, Gernot; Green, MartinConference Paper
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(1998) Corkish, Richard; Sproul, Alistair; Puzzer, Tom; Altermatt, Peter; Heiser, Gernot; Luke, KeungConference Paper
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(1998) Ramadan, Ziad; Byrnes-Preston, Philip; Le-Gia, Thong; Chellen, Vija; Compton, Paul; Mulholland, Mary; Hibbert, D. Brynn; Haddad, Paul; Kang, ByeongConference PaperRipple Down Rules (RDR) is a knowledge acquisition method for knowledge based systems (KBS) which facilitates incremental acquisition of knowledge and ensures that the previous performance of the KBS is not degraded by the incremental addition of the new knowledge. This approach is now well established for single classification tasks and more recently has been extended to multiple classification tasks. This paper describes the further extension of the approach to configuration tasks. The test domain for this study is the configuration of ion chromatography methods in analytical chemistry.
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(1998) Compton, Paul; Ramadan, Ziad; Preston, P; Le-Gia, Thong; Chellen, Vija; Mullholland, M; Hibbert, D. Brynn; Haddad, Paul; Kang, BConference PaperThe major focus of recent knowledge acquisition research has been on problem-solving methods (PSM). This paper present results where a PSM developed for classification has been extended to handle a configuration or parametric design task, designing ion chromatography methods in analytical chemistry. Surprisingly good results have been obtained seemingly because any knowledge that has been added to the knowledge base, has been added precisely to overcome any limitations of the PSM. These results suggest a trade-off between domain knowledge and the power of the PSM and that greater use of domain knowledge would facilitate re-use by allowing PSMs to be used for a broader range of tasks.
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(1998) Milne, LindaConference PaperProducing vegetation maps is one of a myriad of uses that remotely sensed data is being used for. Low error rate classifers can be obtained from the training data generated from surveyed sites and expert knowledge. However, when these classifers are applied to an entire remotely sensed image to produce a map they contain at least many generalisations and at worst gross errors. This is, in part, due to the limited nature of spectral information and limited amounts of training data. In this paper we investigate a technique, called reinforcement classifcation, to generate more accurate classifcations of remotely sensed images. We demonstrate reinforcement classifcation using C4.5 although it is general enough to be applied to any domain and classifcation scheme.