--- abstract: |- This paper addresses the problem of classifying observations when features are context-sensitive, specifically when the testing set involves a context that is different from the training set. The paper begins with a precise definition of the problem, then general strategies are presented for enhancing the performance of classification algorithms on this type of problem. These strategies are tested on two domains. The first domain is the diagnosis of gas turbine engines. The problem is to diagnose a faulty engine in one context, such as warm weather, when the fault has previously been seen only in another context, such as cold weather. The second domain is speech recognition. The problem is to recognize words spoken by a new speaker, not represented in the training set. For both domains, exploiting context results in substantially more accurate classification. altloc: - http://extractor.iit.nrc.ca/publications/NRC-35058.pdf chapter: ~ commentary: ~ commref: ~ confdates: ~ conference: European Conference on Machine Learning confloc: 'Vienna, Austria' contact_email: ~ creators_id: [] creators_name: - family: Turney given: Peter honourific: '' lineage: '' date: 1993 date_type: published datestamp: 2001-11-11 department: ~ dir: disk0/00/00/18/63 edit_lock_since: ~ edit_lock_until: ~ edit_lock_user: ~ editors_id: [] editors_name: [] eprint_status: archive eprintid: 1863 fileinfo: /style/images/fileicons/application_pdf.png;/1863/3/NRC%2D35058.pdf full_text_status: public importid: ~ institution: ~ isbn: ~ ispublished: pub issn: ~ item_issues_comment: [] item_issues_count: 0 item_issues_description: [] item_issues_id: [] item_issues_reported_by: [] item_issues_resolved_by: [] item_issues_status: [] item_issues_timestamp: [] item_issues_type: [] keywords: 'context, robust classification, context-sensitive features, machine learning, robust learning.' lastmod: 2011-03-11 08:54:49 latitude: ~ longitude: ~ metadata_visibility: show note: ~ number: ~ pagerange: 402-407 pubdom: FALSE publication: ~ publisher: ~ refereed: TRUE referencetext: | 1. D.W. Aha, D. Kibler, and M.K. Albert: Instance-based learning algorithms, Machine Learning, 6, 37-66, 1991. 2. D. Kibler, D.W. Aha, and M.K. Albert: Instance-based prediction of real-valued attributes, Computational Intelligence, 5, 51-57, 1989. 3. B.V. Dasarathy: Nearest Neighbor Pattern Classification Techniques, (edited collection), Los Alamitos, CA: IEEE Press, 1991. 4. N.R. Draper and H. Smith: Applied Regression Analysis, (second edition), New York, NY: John Wiley & Sons, 1981. 5. S.E. Fahlman and C. Lebiere: The Cascade-Correlation Learning Architecture, (technical report), CMU-CS-90-100, Pittsburgh, PA: Carnegie-Mellon University, 1991. 6. A.J. Katz, M.T. Gately, and D.R. Collins: Robust classifiers without robust features, Neural Computation, 2, 472-479, 1990. 7. P.D. Turney and M. Halasz: Contextual normalization applied to aircraft gas turbine engine diagnosis, (in press), Journal of Applied Intelligence, 1993. 8. D. Deterding: Speaker Normalization for Automatic Speech Recognition, (Ph.D. thesis), Cambridge, UK: University of Cambridge, Department of Engineering, 1989. 9. A.J. Robinson: Dynamic Error Propagation Networks, (Ph.D. thesis), Cambridge, UK: Uni-versity of Cambridge, Department of Engineering, 1989. 10. P.M. Murphy and D.W. Aha: UCI Repository of Machine Learning Databases, Irvine, CA: University of California, Department of Information and Computer Science, 1991. relation_type: [] relation_uri: [] reportno: ~ rev_number: 12 series: ~ source: ~ status_changed: 2007-09-12 16:41:18 subjects: - comp-sci-art-intel - comp-sci-mach-learn - comp-sci-stat-model succeeds: ~ suggestions: ~ sword_depositor: ~ sword_slug: ~ thesistype: ~ title: Exploiting context when learning to classify type: confpaper userid: 2175 volume: ~