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» On learning with dissimilarity functions
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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...
ICML
2010
IEEE
15 years 4 months ago
Learning optimally diverse rankings over large document collections
Most learning to rank research has assumed that the utility of different documents is independent, which results in learned ranking functions that return redundant results. The fe...
Aleksandrs Slivkins, Filip Radlinski, Sreenivas Go...