ECML
2005
Springer
14 years 5 months ago
2005
Springer
Abstract. While direct, model-free reinforcement learning often performs better than model-based approaches in practice, only the latter have yet supported theoretical guarantees f...
ECML
2005
Springer
14 years 5 months ago
2005
Springer
We propose a new learning algorithm for the set covering machine and a tight data-compression risk bound that the learner can use for choosing the appropriate tradeoff between the ...
ECML
2005
Springer
14 years 5 months ago
2005
Springer
Automatic facial expression recognition is a research topic with interesting applications in the field of human-computer interaction, psychology and product marketing. The classi...
ECML
2005
Springer
14 years 5 months ago
2005
Springer
Active selection of good training examples is an important approach to reducing data-collection costs in machine learning; however, most existing methods focus on maximizing classi...
ECML
2005
Springer
14 years 5 months ago
2005
Springer
Abstract. Classical planning domain representations assume all the objects from one type are exactly the same. But when solving problems in the real world systems, the execution of...
ECML
2005
Springer
14 years 5 months ago
2005
Springer
This paper examines the problem of finding an optimal policy for a Partially Observable Markov Decision Process (POMDP) when the model is not known or is only poorly specified. W...
ECML
2005
Springer
14 years 5 months ago
2005
Springer
Partially Observable Markov Decision Processes (POMDP) provide a standard framework for sequential decision making in stochastic environments. In this setting, an agent takes actio...
ECML
2005
Springer
14 years 5 months ago
2005
Springer
ABSTRACT. Estimating a non-uniformly sampled function from a set of learning points is a classical regression problem. Kernel methods have been widely used in this context, but eve...
ECML
2005
Springer
14 years 5 months ago
2005
Springer
ECML
2005
Springer
14 years 5 months ago
2005
Springer
Probability trees (or Probability Estimation Trees, PET’s) are decision trees with probability distributions in the leaves. Several alternative approaches for learning probabilit...