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ATAL
2006
Springer
15 years 7 months ago
A hierarchical approach to efficient reinforcement learning in deterministic domains
Factored representations, model-based learning, and hierarchies are well-studied techniques for improving the learning efficiency of reinforcement-learning algorithms in large-sca...
Carlos Diuk, Alexander L. Strehl, Michael L. Littm...
ICASSP
2009
IEEE
15 years 7 months ago
Improved lattice-based spoken document retrieval by directly learning from the evaluation measures
Lattice-based approaches have been widely used in spoken document retrieval to handle the speech recognition uncertainty and errors. Position Specific Posterior Lattices (PSPL) an...
Chao-hong Meng, Hung-yi Lee, Lin-shan Lee
135
Voted
JMLR
2012
13 years 5 months ago
Bayesian Comparison of Machine Learning Algorithms on Single and Multiple Datasets
We propose a new method for comparing learning algorithms on multiple tasks which is based on a novel non-parametric test that we call the Poisson binomial test. The key aspect of...
Alexandre Lacoste, François Laviolette, Mar...
112
Voted
BMCBI
2006
124views more  BMCBI 2006»
15 years 3 months ago
Detection of divergent genes in microbial aCGH experiments
Background: Array-based comparative genome hybridization (aCGH) is a tool for rapid comparison of genomes from different bacterial strains. The purpose of such analysis is to dete...
Lars Snipen, Dirk Repsilber, Ludvig Nyquist, &Arin...
ICML
2005
IEEE
16 years 4 months ago
Predictive low-rank decomposition for kernel methods
Low-rank matrix decompositions are essential tools in the application of kernel methods to large-scale learning problems. These decompositions have generally been treated as black...
Francis R. Bach, Michael I. Jordan