DYNAMIC BIAS IS NECESSARY IN REAL WORLD

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dc.contributor.authorHeise, Rosannaeng
dc.contributor.authorMacDonald, Bruce A.eng
dc.date.accessioned2008-02-26T22:38:30Z
dc.date.available2008-02-26T22:38:30Z
dc.date.computerscience1999-05-27eng
dc.date.issued1991-04-01eng
dc.description.abstractThis paper discusses the bias present in machine learning systems, emphasizing its effect on learnability and complexity. A good bias must allow more concepts to be learned and/or decrease the complexity associated with learning. The paper develops an exhaustive framework for bias, with two important distinctions: \fIstatic\fR versus \fIdynamic\fR and \fIfocus\fR versus \fImagnify\fR. The well-known candidate elimination algorithm (Mitchell) is used to illustrate the framework. Real world learners need dynamic bias. The paper examines two representative systems. \s+2S\s-2TABB (Utgoff) dynamically magnifies the description space where learning would otherwise be impossible. \s+2E\s-2TAR is a prototype for learning robot assembly tasks from examples--a dynamic focusing mechanism reduces both the real world description space and the task construction complexity. Inductive learning must be viewed as a problem of dynamic search control.eng
dc.description.notesWe are currently acquiring citations for the work deposited into this collection. We recognize the distribution rights of this item may have been assigned to another entity, other than the author(s) of the work.If you can provide the citation for this work or you think you own the distribution rights to this work please contact the Institutional Repository Administrator at digitize@ucalgary.caeng
dc.identifier.department1991-428-12eng
dc.identifier.doihttp://dx.doi.org/10.11575/PRISM/30888
dc.identifier.urihttp://hdl.handle.net/1880/45580
dc.language.isoEngeng
dc.publisher.corporateUniversity of Calgaryeng
dc.publisher.facultyScienceeng
dc.subjectComputer Scienceeng
dc.titleDYNAMIC BIAS IS NECESSARY IN REAL WORLDeng
dc.typeunknown
thesis.degree.disciplineComputer Scienceeng
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