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ALT
1999
Springer
15 years 8 months ago
PAC Learning with Nasty Noise
We introduce a new model for learning in the presence of noise, which we call the Nasty Noise model. This model generalizes previously considered models of learning with noise. Th...
Nader H. Bshouty, Nadav Eiron, Eyal Kushilevitz
CVPR
2008
IEEE
16 years 6 months ago
The Logistic Random Field - A convenient graphical model for learning parameters for MRF-based labeling
Graphical models are fundamental tools for modeling images and other applications. In this paper, we propose the Logistic Random Field (LRF) model for representing a discrete-valu...
Marshall F. Tappen, Kegan G. G. Samuel, Craig V. D...
133
Voted
LSO
2004
Springer
15 years 9 months ago
Embedding Experiences in Micro-didactical Arrangements
Experience-based Information Systems (EbIS) enable organizations to capture, store and reuse knowledge and experiences for continuous competence development. However, there are sev...
Eric Ras, Stephan Weibelzahl
ICML
2000
IEEE
16 years 4 months ago
Hierarchical Unsupervised Learning
We consider the problem of unsupervised classification of temporal sequences of facial expressions in video. This problem arises in the design of an adaptive visual agent, which m...
Shivakumar Vaithyanathan, Byron Dom
COLT
1993
Springer
15 years 8 months ago
Parameterized Learning Complexity
We describe three applications in computational learning theory of techniques and ideas recently introduced in the study of parameterized computational complexity. (1) Using param...
Rodney G. Downey, Patricia A. Evans, Michael R. Fe...