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ICMLA
2009
13 years 7 months ago
Learning Deep Neural Networks for High Dimensional Output Problems
State-of-the-art pattern recognition methods have difficulty dealing with problems where the dimension of the output space is large. In this article, we propose a new framework ba...
Benjamin Labbé, Romain Hérault, Cl&e...
EC
2010
176views ECommerce» more  EC 2010»
13 years 6 months ago
Learning Factorizations in Estimation of Distribution Algorithms Using Affinity Propagation
Estimation of distribution algorithms (EDAs) that use marginal product model factorizations have been widely applied to a broad range of, mainly binary, optimization problems. In ...
Roberto Santana, Pedro Larrañaga, Jos&eacut...
IPSN
2010
Springer
13 years 6 months ago
i-MAC - a MAC that learns
Traffic patterns in manufacturing machines exhibit strong temporal correlations due to the underlying repetitive nature of their operations. A MAC protocol can potentially learn t...
Krishna Kant Chintalapudi
CVPR
2005
IEEE
14 years 11 months ago
Semi-Supervised Learning Based Object Detection in Aerial Imagery
Object detection in aerial imagery has been well studied in computer vision for years. However, given the complexity of large variations of the appearance of the object and the ba...
Jian Yao, Zhongfei (Mark) Zhang
IJCAI
1989
13 years 10 months ago
Learning to Diagnose by Doing
This paper is a study on the process of evolution of a novice to an expert in a diagnostic context. In er, we have chosen an abstract example of a diagnostic problem. The results ...
Jayant Kalagnanam, Eswaran Subrahmanian