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» Iterative Learning Control - Monotonicity and Optimization
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RECSYS
2009
ACM
14 years 14 days ago
Recommending new movies: even a few ratings are more valuable than metadata
In the Netflix Prize competition many new collaborative filtering (CF) approaches emerged, which are excellent in optimizing the RMSE of the predictions. Matrix factorization (M...
István Pilászy, Domonkos Tikk
APIN
1998
107views more  APIN 1998»
13 years 5 months ago
Multiple Adaptive Agents for Tactical Driving
Abstract. Recent research in automated highway systems has ranged from low-level vision-based controllers to high-level route-guidance software. However, there is currently no syst...
Rahul Sukthankar, Shumeet Baluja, John Hancock
GECCO
2007
Springer
183views Optimization» more  GECCO 2007»
13 years 9 months ago
Evolving distributed agents for managing air traffic
Air traffic management offers an intriguing real world challenge to designing large scale distributed systems using evolutionary computation. The ability to evolve effective air t...
Adrian K. Agogino, Kagan Tumer
GECCO
2009
Springer
128views Optimization» more  GECCO 2009»
14 years 16 days ago
Neural network ensembles for time series forecasting
This work provides an analysis of using the evolutionary algorithm EPNet to create ensembles of artificial neural networks to solve a range of forecasting tasks. Several previous...
Victor M. Landassuri-Moreno, John A. Bullinaria
GECCO
2009
Springer
199views Optimization» more  GECCO 2009»
13 years 10 months ago
Using behavioral exploration objectives to solve deceptive problems in neuro-evolution
Encouraging exploration, typically by preserving the diversity within the population, is one of the most common method to improve the behavior of evolutionary algorithms with dece...
Jean-Baptiste Mouret, Stéphane Doncieux