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» MABLE: a framework for learning from natural instruction
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UAI
2000
14 years 11 months ago
Gaussian Process Networks
In this paper we address the problem of learning the structure of a Bayesian network in domains with continuous variables. This task requires a procedure for comparing different c...
Nir Friedman, Iftach Nachman
CVPR
2009
IEEE
16 years 4 months ago
Holistic Context Modeling using Semantic Co-occurrences
We present a simple framework to model contextual relationships between visual concepts. The new framework combines ideas from previous object-centric methods (which model conte...
Nikhil Rasiwasia (University Of California, San Di...
CRV
2009
IEEE
115views Robotics» more  CRV 2009»
15 years 4 months ago
Learning Model Complexity in an Online Environment
In this paper we introduce the concept and method for adaptively tuning the model complexity in an online manner as more examples become available. Challenging classification pro...
Dan Levi, Shimon Ullman
ICSM
2005
IEEE
15 years 3 months ago
Design Pattern Mining Enhanced by Machine Learning
Design patterns present good solutions to frequently occurring problems in object-oriented software design. Thus their correct application in a system’s design may significantl...
Rudolf Ferenc, Árpád Beszédes...
ICCV
2007
IEEE
15 years 11 months ago
Learning Higher-order Transition Models in Medium-scale Camera Networks
We present a Bayesian framework for learning higherorder transition models in video surveillance networks. Such higher-order models describe object movement between cameras in the...
Ryan Farrell, David S. Doermann, Larry S. Davis