Abstract
Techniques for the logical analysis of binary data have successfully been applied to non-binary data which has been 'binarized' by means of cutpoints; see Boros et al. (1997, 2000) [7,8]. In this paper, we analyze the predictive performance of such techniques and, in particular, we derive generalization error bounds that depend on how 'robust' the cutpoints are.
Original language | English |
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Pages (from-to) | 355-364 |
Number of pages | 10 |
Journal | Discrete Applied Mathematics |
Volume | 160 |
Issue number | 4-5 |
DOIs | |
State | Published - 1 Mar 2012 |
Externally published | Yes |
Keywords
- Generalization error
- LAD methods
- Learning algorithms
- Logical analysis of data
- Machine learning
ASJC Scopus subject areas
- Discrete Mathematics and Combinatorics
- Applied Mathematics