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COLT
2008
Springer
15 years 8 months ago
Learning in the Limit with Adversarial Disturbances
We study distribution-dependent, data-dependent, learning in the limit with adversarial disturbance. We consider an optimization-based approach to learning binary classifiers from...
Constantine Caramanis, Shie Mannor
NIPS
2004
15 years 8 months ago
Brain Inspired Reinforcement Learning
Successful application of reinforcement learning algorithms often involves considerable hand-crafting of the necessary non-linear features to reduce the complexity of the value fu...
François Rivest, Yoshua Bengio, John Kalask...
ECAI
2010
Springer
15 years 7 months ago
Case-Based Multiagent Reinforcement Learning: Cases as Heuristics for Selection of Actions
This work presents a new approach that allows the use of cases in a case base as heuristics to speed up Multiagent Reinforcement Learning algorithms, combining Case-Based Reasoning...
Reinaldo A. C. Bianchi, Ramon López de M&aa...
COMPLEXITY
2008
84views more  COMPLEXITY 2008»
15 years 6 months ago
Evolutionary learning of small networks
Results are presented of a simulation which mimics an evolutionary learning process for small networks. Special features of these networks include a high recurrency, a transition ...
Thomas Filk, Albrecht von Müller
IPL
2010
92views more  IPL 2010»
15 years 5 months ago
Learning parities in the mistake-bound model
We study the problem of learning parity functions that depend on at most k variables (kparities) attribute-efficiently in the mistake-bound model. We design a simple, deterministi...
Harry Buhrman, David García-Soriano, Arie M...