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» Real-World Limits to Algorithmic Intelligence
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ECAI
2008
Springer
14 years 11 months ago
MTForest: Ensemble Decision Trees based on Multi-Task Learning
Many ensemble methods, such as Bagging, Boosting, Random Forest, etc, have been proposed and widely used in real world applications. Some of them are better than others on noisefre...
Qing Wang, Liang Zhang, Mingmin Chi, Jiankui Guo
AAAI
2010
14 years 11 months ago
G-Optimal Design with Laplacian Regularization
In many real world applications, labeled data are usually expensive to get, while there may be a large amount of unlabeled data. To reduce the labeling cost, active learning attem...
Chun Chen, Zhengguang Chen, Jiajun Bu, Can Wang, L...
FLAIRS
2006
14 years 11 months ago
Simulated Visual Perception-Based Control for Autonomous Mobile Agents
Autonomous robots, such as automatic vacuum cleaners, toy robot dogs, and autonomous vehicles for the military, are rapidly becoming a part of everyday life. As a result the need ...
Daniel Flower, Burkhard Wünsche, Hans W. Gues...
ATAL
2009
Springer
15 years 4 months ago
An empirical analysis of value function-based and policy search reinforcement learning
In several agent-oriented scenarios in the real world, an autonomous agent that is situated in an unknown environment must learn through a process of trial and error to take actio...
Shivaram Kalyanakrishnan, Peter Stone
ATAL
2010
Springer
14 years 11 months ago
Deception in networks of mobile sensing agents
Recent studies have investigated how a team of mobile sensors can cope with real world constraints, such as uncertainty in the reward functions, dynamically appearing and disappea...
Viliam Lisý, Roie Zivan, Katia P. Sycara, M...