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» Learning from Multiple Annotators with Gaussian Processes
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DSMML
2004
Springer
15 years 5 months ago
Multi Channel Sequence Processing
Abstract. This paper summarizes some of the current research challenges arising from multi-channel sequence processing. Indeed, multiple real life applications involve simultaneous...
Samy Bengio, Hervé Bourlard
CVPR
2012
IEEE
13 years 2 months ago
Batch mode Adaptive Multiple Instance Learning for computer vision tasks
Multiple Instance Learning (MIL) has been widely exploited in many computer vision tasks, such as image retrieval, object tracking and so on. To handle ambiguity of instance label...
Wen Li, Lixin Duan, Ivor Wai-Hung Tsang, Dong Xu
AAAI
2011
13 years 12 months ago
Learned Behaviors of Multiple Autonomous Agents in Smart Grid Markets
One proposed approach to managing a large complex Smart Grid is through Broker Agents who buy electrical power from distributed producers, and also sell power to consumers, via a ...
Prashant P. Reddy, Manuela M. Veloso
GECCO
2006
Springer
159views Optimization» more  GECCO 2006»
15 years 3 months ago
Smart crossover operator with multiple parents for a Pittsburgh learning classifier system
This paper proposes a new smart crossover operator for a Pittsburgh Learning Classifier System. This operator, unlike other recent LCS approaches of smart recombination, does not ...
Jaume Bacardit, Natalio Krasnogor
CORR
2006
Springer
86views Education» more  CORR 2006»
14 years 12 months ago
Optimal Distortion-Power Tradeoffs in Sensor Networks: Gauss-Markov Random Processes
We investigate the optimal performance of dense sensor networks by studying the joint source-channel coding problem. The overall goal of the sensor network is to take measurements ...
Nan Liu, Sennur Ulukus