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JGO
2010
117views more  JGO 2010»
15 years 2 months ago
Machine learning problems from optimization perspective
Both optimization and learning play important roles in a system for intelligent tasks. On one hand, we introduce three types of optimization tasks studied in the machine learning l...
Lei Xu
ML
2010
ACM
151views Machine Learning» more  ML 2010»
15 years 2 months ago
Inductive transfer for learning Bayesian networks
In several domains it is common to have data from different, but closely related problems. For instance, in manufacturing, many products follow the same industrial process but with...
Roger Luis, Luis Enrique Sucar, Eduardo F. Morales
CVPR
2007
IEEE
16 years 6 months ago
Learning Gaussian Conditional Random Fields for Low-Level Vision
Markov Random Field (MRF) models are a popular tool for vision and image processing. Gaussian MRF models are particularly convenient to work with because they can be implemented u...
Marshall F. Tappen, Ce Liu, Edward H. Adelson, Wil...
WWW
2005
ACM
16 years 5 months ago
Ontology-based learning content repurposing
This paper investigates basic research issues that need to be addressed for developing an architecture that enables repurposing of learning objects in a flexible way. Currently, t...
Katrien Verbert, Dragan Gasevic, Jelena Jovanovic,...
137
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APPROX
2005
Springer
80views Algorithms» more  APPROX 2005»
15 years 10 months ago
On Learning Random DNF Formulas Under the Uniform Distribution
Abstract: We study the average-case learnability of DNF formulas in the model of learning from uniformly distributed random examples. We define a natural model of random monotone ...
Jeffrey C. Jackson, Rocco A. Servedio