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The challenges of ignorance

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dc.contributor.author Barnard, E
dc.date.accessioned 2012-02-15T09:03:02Z
dc.date.available 2012-02-15T09:03:02Z
dc.date.issued 2009-11
dc.identifier.citation Barnard, E. The challenges of ignorance. 20th Annual Symposium of the Pattern Recognition Association of South Africa (PRASA), Stellenbosch, South Africa, 30 November-01 December 2009, pp 7-10 en_US
dc.identifier.isbn 978-0-7992-2356-9
dc.identifier.uri http://www.prasa.org/proceedings/2009/prasa09-02.pdf
dc.identifier.uri http://hdl.handle.net/10204/5571
dc.description 20th Annual Symposium of the Pattern Recognition Association of South Africa (PRASA), Stellenbosch, South Africa, 30 November-01 December 2009 en_US
dc.description.abstract The authors have previously argued that the infamous "No Free Lunch" theorem for supervised learning is a paradoxical result of a misleading choice of prior probabilities. Here, they provide more analysis of the dangers of uniform densities as ignorance models, and point out the need for a framework that allows for prior probabilities to be constructed in a more principled fashion. Such a framework is proposed for the task of supervised learning, based on the trend of the Bayes error as a function of the number of features employed. Experimental measurements on a number of standard classification tasks confirm the representational utility of the proposed approach. en_US
dc.language.iso en en_US
dc.publisher PRASA en_US
dc.subject Ignorance models en_US
dc.subject Supervised learning en_US
dc.subject Bayes error en_US
dc.title The challenges of ignorance en_US
dc.type Conference Presentation en_US
dc.identifier.apacitation Barnard, E. (2009). The challenges of ignorance. PRASA. http://hdl.handle.net/10204/5571 en_ZA
dc.identifier.chicagocitation Barnard, E. "The challenges of ignorance." (2009): http://hdl.handle.net/10204/5571 en_ZA
dc.identifier.vancouvercitation Barnard E, The challenges of ignorance; PRASA; 2009. http://hdl.handle.net/10204/5571 . en_ZA
dc.identifier.ris TY - Conference Presentation AU - Barnard, E AB - The authors have previously argued that the infamous "No Free Lunch" theorem for supervised learning is a paradoxical result of a misleading choice of prior probabilities. Here, they provide more analysis of the dangers of uniform densities as ignorance models, and point out the need for a framework that allows for prior probabilities to be constructed in a more principled fashion. Such a framework is proposed for the task of supervised learning, based on the trend of the Bayes error as a function of the number of features employed. Experimental measurements on a number of standard classification tasks confirm the representational utility of the proposed approach. DA - 2009-11 DB - ResearchSpace DP - CSIR KW - Ignorance models KW - Supervised learning KW - Bayes error LK - https://researchspace.csir.co.za PY - 2009 SM - 978-0-7992-2356-9 T1 - The challenges of ignorance TI - The challenges of ignorance UR - http://hdl.handle.net/10204/5571 ER - en_ZA


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