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ICONIP
2009
13 years 6 months ago
Learning Gaussian Process Models from Uncertain Data
It is generally assumed in the traditional formulation of supervised learning that only the outputs data are uncertain. However, this assumption might be too strong for some learni...
Patrick Dallaire, Camille Besse, Brahim Chaib-draa
ALT
2008
Springer
14 years 6 months ago
Numberings Optimal for Learning
This paper extends previous studies on learnability in non-acceptable numberings by considering the question: for which criteria which numberings are optimal, that is, for which nu...
Sanjay Jain, Frank Stephan
EUROGP
2009
Springer
132views Optimization» more  EUROGP 2009»
14 years 3 months ago
A Statistical Learning Perspective of Genetic Programming
Code bloat, the excessive increase of code size, is an important issue in Genetic Programming (GP). This paper proposes a theoretical analysis of code bloat in GP from the perspec...
Nur Merve Amil, Nicolas Bredeche, Christian Gagn&e...
EDM
2009
179views Data Mining» more  EDM 2009»
13 years 6 months ago
Learning Factors Transfer Analysis: Using Learning Curve Analysis to Automatically Generate Domain Models
This paper describes a novel method to create a quantitative model of an educational content domain of related practice item-types using learning curves. By using a pairwise test t...
Philip I. Pavlik Jr., Hao Cen, Kenneth R. Koedinge...
CIVR
2006
Springer
181views Image Analysis» more  CIVR 2006»
14 years 21 days ago
Image Searching and Browsing by Active Aspect-Based Relevance Learning
Aspect-based relevance learning is a relevance feedback scheme based on a natural model of relevance in terms of image aspects. In this paper we propose a number of active learning...
Mark J. Huiskes