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» Learning Models for Object Recognition
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ICIP
2000
IEEE
16 years 3 months ago
Hierarchical Image Probability (HIP) Models
We formulate a model for probability distributions on image spaces. We show that any distribution of images can be factored exactly into conditional distributions of feature vecto...
Clay Spence, Lucas C. Parra, Paul Sajda
96
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CIDM
2007
IEEE
15 years 5 months ago
Efficient Kernel-based Learning for Trees
Kernel methods are effective approaches to the modeling of structured objects in learning algorithms. Their major drawback is the typically high computational complexity of kernel ...
Fabio Aiolli, Giovanni Da San Martino, Alessandro ...
140
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IJCAI
1993
15 years 3 months ago
Learning Decision Lists over Tree Patterns and Its Application
This paper introduces a new concept, a decision tree (or list) over tree patterns, which is a natural extension of a decision tree (or decision list), for dealing with tree struct...
Satoshi Kobayashi, Koichi Hori, Setsuo Ohsuga
119
Voted
JMLR
2006
117views more  JMLR 2006»
15 years 1 months ago
On the Complexity of Learning Lexicographic Strategies
Fast and frugal heuristics are well studied models of bounded rationality. Psychological research has proposed the take-the-best heuristic as a successful strategy in decision mak...
Michael Schmitt, Laura Martignon
138
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CVPR
2009
IEEE
16 years 9 months ago
Convexity and Bayesian Constrained Local Models
The accurate localization of facial features plays a fundamental role in any face recognition pipeline. Constrained local models (CLM) provide an effective approach to localizati...
Ulrich Paquet (Imense Ltd)