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ICML
2003
IEEE
15 years 15 days ago
Design for an Optimal Probe
Michael O. Duff
ICML
2003
IEEE
15 years 15 days ago
Relational Instance Based Regression for Relational Reinforcement Learning
Relational reinforcement learning (RRL) is a Q-learning technique which uses first order regression techniques to generalize the Qfunction. Both the relational setting and the Q-l...
Kurt Driessens, Jan Ramon
ICML
2003
IEEE
15 years 15 days ago
On Kernel Methods for Relational Learning
Kernel methods have gained a great deal of popularity in the machine learning community as a method to learn indirectly in highdimensional feature spaces. Those interested in rela...
Chad M. Cumby, Dan Roth
ICML
2003
IEEE
15 years 15 days ago
Tractable Bayesian Learning of Tree Augmented Naive Bayes Models
Bayesian classifiers such as Naive Bayes or Tree Augmented Naive Bayes (TAN) have shown excellent performance given their simplicity and heavy underlying independence assumptions....
Jesús Cerquides, Ramon López de M&aa...
ICML
2003
IEEE
15 years 15 days ago
Semi-Supervised Learning of Mixture Models
This paper analyzes the performance of semisupervised learning of mixture models. We show that unlabeled data can lead to an increase in classification error even in situations wh...
Fabio Gagliardi Cozman, Ira Cohen, Marcelo Cesar C...
ICML
2003
IEEE
15 years 15 days ago
BL-WoLF: A Framework For Loss-Bounded Learnability In Zero-Sum Games
We present BL-WoLF, a framework for learnability in repeated zero-sum games where the cost of learning is measured by the losses the learning agent accrues (rather than the number...
Vincent Conitzer, Tuomas Sandholm
ICML
2003
IEEE
15 years 15 days ago
AWESOME: A General Multiagent Learning Algorithm that Converges in Self-Play and Learns a Best Response Against Stationary Oppon
A satisfactory multiagent learning algorithm should, at a minimum, learn to play optimally against stationary opponents and converge to a Nash equilibrium in self-play. The algori...
Vincent Conitzer, Tuomas Sandholm
ICML
2003
IEEE
15 years 15 days ago
The Use of the Ambiguity Decomposition in Neural Network Ensemble Learning Methods
We analyze the formal grounding behind Negative Correlation (NC) Learning, an ensemble learning technique developed in the evolutionary computation literature. We show that by rem...
Gavin Brown, Jeremy L. Wyatt