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ICAC
2008
IEEE
16 years 1 months ago
Utility-Based Reinforcement Learning for Reactive Grids
—Large scale production grids are an important case for autonomic computing. They follow a mutualization paradigm: decision-making (human or automatic) is distributed and largely...
Julien Perez, Cécile Germain-Renaud, Bal&aa...
STOC
1993
ACM
141views Algorithms» more  STOC 1993»
15 years 11 months ago
Bounds for the computational power and learning complexity of analog neural nets
Abstract. It is shown that high-order feedforward neural nets of constant depth with piecewisepolynomial activation functions and arbitrary real weights can be simulated for Boolea...
Wolfgang Maass
JASIS
2006
96views more  JASIS 2006»
15 years 7 months ago
Learning to classify documents according to genre
Genre or style analysis can be used to improve results achieved using standard IR techniques. A genre class is a group of documents that are written in a similar style. Genre clas...
Aidan Finn, Nicholas Kushmerick
AIME
2009
Springer
15 years 5 months ago
Segmentation of Lung Tumours in Positron Emission Tomography Scans: A Machine Learning Approach
Lung cancer represents the most deadly type of malignancy. In this work we propose a machine learning approach to segmenting lung tumours in Positron Emission Tomography (PET) scan...
Aliaksei Kerhet, Cormac Small, Harvey Quon, Terenc...
AAAI
2011
14 years 7 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