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ICML
2005
IEEE
16 years 8 months ago
Learning to rank using gradient descent
We investigate using gradient descent methods for learning ranking functions; we propose a simple probabilistic cost function, and we introduce RankNet, an implementation of these...
Christopher J. C. Burges, Tal Shaked, Erin Renshaw...
ICML
2005
IEEE
16 years 8 months ago
A practical generalization of Fourier-based learning
This paper presents a search algorithm for finding functions that are highly correlated with an arbitrary set of data. The functions found by the search can be used to approximate...
Adam Drake, Dan Ventura
ICML
2005
IEEE
16 years 8 months ago
Predicting good probabilities with supervised learning
We examine the relationship between the predictions made by different learning algorithms and true posterior probabilities. We show that maximum margin methods such as boosted tre...
Alexandru Niculescu-Mizil, Rich Caruana
ICML
2005
IEEE
16 years 8 months ago
Identifying useful subgoals in reinforcement learning by local graph partitioning
We present a new subgoal-based method for automatically creating useful skills in reinforcement learning. Our method identifies subgoals by partitioning local state transition gra...
Özgür Simsek, Alicia P. Wolfe, Andrew G....
ICML
2005
IEEE
16 years 8 months ago
New d-separation identification results for learning continuous latent variable models
Learning the structure of graphical models is an important task, but one of considerable difficulty when latent variables are involved. Because conditional independences using hid...
Ricardo Silva, Richard Scheines