DSpace
 

Researchspace >
General science, engineering & technology >
General science, engineering & technology >
General science, engineering & technology >

Please use this identifier to cite or link to this item: http://hdl.handle.net/10204/5571

Title: The challenges of ignorance
Authors: Barnard, E
Keywords: Ignorance models
Supervised learning
Bayes error
Issue Date: Nov-2009
Publisher: PRASA
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
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.
Description: 20th Annual Symposium of the Pattern Recognition Association of South Africa (PRASA), Stellenbosch, South Africa, 30 November-01 December 2009
URI: http://www.prasa.org/proceedings/2009/prasa09-02.pdf
http://hdl.handle.net/10204/5571
ISBN: 978-0-7992-2356-9
Appears in Collections:Advanced mathematical modelling and simulation
Digital intelligence
Mobile intelligent autonomous systems
General science, engineering & technology

Files in This Item:

File Description SizeFormat
Barnard2_2009.pdf2.86 MBAdobe PDFView/Open
View Statistics

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

 

Valid XHTML 1.0! DSpace Software Copyright © 2002-2010  Duraspace - Feedback