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ECCV
2004
Springer
15 years 11 months ago
A Statistical Model for General Contextual Object Recognition
We consider object recognition as the process of attaching meaningful labels to specific regions of an image, and propose a model that learns spatial relationships between objects....
Peter Carbonetto, Nando de Freitas, Kobus Barnard
CVPR
2007
IEEE
15 years 11 months ago
Utilizing Variational Optimization to Learn Markov Random Fields
Markov Random Field, or MRF, models are a powerful tool for modeling images. While much progress has been made in algorithms for inference in MRFs, learning the parameters of an M...
Marshall F. Tappen
ALT
1997
Springer
15 years 2 months ago
Learning DFA from Simple Examples
Efficient learning of DFA is a challenging research problem in grammatical inference. It is known that both exact and approximate (in the PAC sense) identifiability of DFA is har...
Rajesh Parekh, Vasant Honavar
NIPS
1990
14 years 11 months ago
Bumptrees for Efficient Function, Constraint and Classification Learning
A new class of data structures called "bumptrees" is described. These structures are useful for efficiently implementing a number of neural network related operations. A...
Stephen M. Omohundro
IJCAI
2007
14 years 11 months ago
Maximum Margin Coresets for Active and Noise Tolerant Learning
We study the problem of learning large margin halfspaces in various settings using coresets to show that coresets are a widely applicable tool for large margin learning. A large m...
Sariel Har-Peled, Dan Roth, Dav Zimak