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» On the Complexity of Function Learning
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KDD
2008
ACM
172views Data Mining» more  KDD 2008»
16 years 6 months ago
Structured metric learning for high dimensional problems
The success of popular algorithms such as k-means clustering or nearest neighbor searches depend on the assumption that the underlying distance functions reflect domain-specific n...
Jason V. Davis, Inderjit S. Dhillon
150
Voted
ECML
2004
Springer
15 years 11 months ago
Batch Reinforcement Learning with State Importance
Abstract. We investigate the problem of using function approximation in reinforcement learning where the agent’s policy is represented as a classifier mapping states to actions....
Lihong Li, Vadim Bulitko, Russell Greiner
155
Voted
STOC
1993
ACM
117views Algorithms» more  STOC 1993»
15 years 10 months ago
Efficient noise-tolerant learning from statistical queries
In this paper, we study the problem of learning in the presence of classification noise in the probabilistic learning model of Valiant and its variants. In order to identify the cl...
Michael J. Kearns
ACMICEC
2006
ACM
110views ECommerce» more  ACMICEC 2006»
15 years 10 months ago
Learning inventory management strategies for commodity supply chains with customer satisfaction
In this paper, we look at a supply chain of commodity goods where customer demand is uncertain and partly based on reputation, and where raw material replenishment is uncertain in...
Jeroen van Luin, Han La Poutré, J. Will M. ...
164
Voted
ECML
2006
Springer
15 years 9 months ago
Learning Stochastic Tree Edit Distance
Trees provide a suited structural representation to deal with complex tasks such as web information extraction, RNA secondary structure prediction, or conversion of tree structured...
Marc Bernard, Amaury Habrard, Marc Sebban