Convex hulls as an hypothesis language bias

Newlands, D. A. and Webb, G. I. 2003, Convex hulls as an hypothesis language bias, in DM IV 2003 : Proceedings of the Fourth International Conference on Data Mining : Data Mining IV, WIT Press, Southampton, England, pp. 285-294.

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Title Convex hulls as an hypothesis language bias
Author(s) Newlands, D. A.
Webb, G. I.
Conference name International Conference on Data Mining (4th : 2003 : Rio de Janeiro, Brazil)
Conference location Rio de Janeiro, Brazil
Conference dates 1-3 Dec. 2003
Title of proceedings DM IV 2003 : Proceedings of the Fourth International Conference on Data Mining : Data Mining IV
Editor(s) Ebecken, N. F. F
Brebbia , C. A.
Zanasi, A.
Publication date 2003
Start page 285
End page 294
Total pages 670, [2] p.
Publisher WIT Press
Place of publication Southampton, England
Summary Classification learning is dominated by systems which induce large numbers of small axis-orthogonal decision surfaces which biases such systems towards particular hypothesis types. However, there is reason to believe that many domains have underlying concepts which do not involve axis orthogonal surfaces. Further, the multiplicity of small decision regions mitigates against any holistic appreciation of the theories produced by these systems, notwithstanding the fact that many of the small regions are individually comprehensible. We propose the use of less strongly biased hypothesis languages which might be expected to model' concepts using a number of structures close to the number of actual structures in the domain. An instantiation of such a language, a convex hull based classifier, CHI, has been implemented to investigate modeling concepts as a small number of large geometric structures in n-dimensional space. A comparison of the number of regions induced is made against other well-known systems on a representative selection of largely or wholly continuous valued machine learning tasks. The convex hull system is shown to produce a number of induced regions about an order of magnitude less than well-known systems and very close to the number of actual concepts. This representation, as convex hulls, allows the possibility of extraction of higher level mathematical descriptions of the induced concepts, using the techniques of computational geometry.
ISBN 1853128066
Language eng
Field of Research 080199 Artificial Intelligence and Image Processing not elsewhere classified
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1 Full written paper - refereed
HERDC collection year 2004
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