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dc.contributor.authorWhigham, Peter Aen_NZ
dc.date.available2011-04-07T03:01:50Z
dc.date.copyright2004-11en_NZ
dc.identifier.citationWhigham, P. A. (2004). The use of boundary conditions for inductive models (pp. 63–70). Presented at the 16th Annual Colloquium of the Spatial Information Research Centre (SIRC 2004: A Spatio-temporal Workshop).en
dc.identifier.urihttp://hdl.handle.net/10523/688
dc.description.abstractThere is a large amount of interest in creating models from data using a variety of machine learning methods. Most of these approaches require a good distribution of observed values to produce reliable models. The use of background knowledge to augment the observed values has also been explored as a method to supplement the original feature set of training data. This paper argues that there is an additional set of data that can be created for many types of problems, based on the concept of boundary conditions. This boundary data incorporates an understanding of the modeled system behaviour under certain extreme values and therefore reduces the degrees of freedom within the inferred model. This paper argues that by using this information when training an inductive model a more robust generalization of the data can be achieved under some circumstances.en_NZ
dc.format.mimetypeapplication/pdf
dc.relation.urihttp://www.business.otago.ac.nz/SIRC05/conferences/2004/16_Whigham.pdfen_NZ
dc.subjectinductionen_NZ
dc.subjectbackground knowledgeen_NZ
dc.subjectboundary conditionsen_NZ
dc.subject.lcshQA75 Electronic computers. Computer scienceen_NZ
dc.subject.lcshQA76 Computer softwareen_NZ
dc.titleThe use of boundary conditions for inductive modelsen_NZ
dc.typeConference or Workshop Item (Paper)en_NZ
dc.description.versionPublisheden_NZ
otago.date.accession2005-12-05en_NZ
otago.relation.pages63-70en_NZ
otago.openaccessOpen
dc.identifier.eprints139en_NZ
dc.description.refereedNon Peer Revieweden_NZ
otago.school.eprintsSpatial Information Research Centreen_NZ
otago.school.eprintsInformation Scienceen_NZ
dc.description.referencesHirsh, H. & M. Noordewier (1994) Using Background Knowledge to Improve Inductive Learning. IEEE Intelligent Systems, 9:5, pp. 3-6. Kanevski, M., R. Parkin, A. Pozdnukhov, V. Timonin, M. Maignan, B. Yatsalo & S. Sanu (2002) Environmental Data Mining and Modelling Based on Machine Learning Algorithms and Geostatistics. Integrated Assessment and Decision Support: Proceedings of the 1st Biennial Meeting of the iEMSs, . Langley, P. (1986) On Machine Learning. Machine Learning, 1:1, pp. 5-10. Lenat, D. 1984, 'The Role of Heuristics in Learning by Discovery: Three Case Studies', in Machine Learning: An Artificial Intelligence Approach, Ed R. S. a. C. Michalski, J.G. and Mitchell,T.M, pp. 243-306. Muggleton, S. & W. Buntine 1992, 'Machine Invention of First-Order Predicates by Inverting Resolution', in Inductive Logic Programming, Ed S. Muggleton, pp. 261-281. Ozesmi, S. & U. Ozesmi (1999) An artificial neural network approach to spatial habitat modelling with interspecific interaction. Ecological Modelling, 116: pp. 15-31. Ozesmi, U. & W. Mitsch (1997) A spatial habitat model for the marsh-breeding red-winged blackbird (Agelaius phoeniceus L.) in coastal Lake Erie wetlands. Ecological Modelling, 101: pp. 139-152. Rendell, L. & H. Cho (1990) Empirical learning as a function of concept character. Machine Learning, 5:3, pp. 267-298. Sarle, W. S. 1999, Donoho-Johnstone benchmarks: neural net results, fpt://ftp.sas.com/pub/neural/dojo/dojo.html, Last accessed 1/10/2004. Silvert, W. & M. Baptist 1998, 'Can Neural Networks be used in Data-Poor Situations?' in Artificial Neuronal Networks: Application to Ecology and Evolution, Eds S. Lek & J. Guegan, Springer-Verlag, Berlin, pp. 241-248. Tribou, E. & P. Nobel 2004, Neuroet: a simple artificial neural network for scientists, Civil and Environmental Engineering, University of Washington, City, pp 43. Utgoff, P. 1986, 'Shift of bias for inductive concept learning', in Machine Learning: An Artificial Intelligence Approach Morgan Kaufmann Publishers, Inc. Los Altos, CA, pp. 107-148.en_NZ
otago.event.dates29-30 November 2004en_NZ
otago.event.placeDunedin, New Zealanden_NZ
otago.event.typeconferenceen_NZ
otago.event.title16th Annual Colloquium of the Spatial Information Research Centre (SIRC 2004: A Spatio-temporal Workshop)en_NZ
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