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» Machine Learning by Function Decomposition
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ICML
2003
IEEE
16 years 5 months ago
Using Linear-threshold Algorithms to Combine Multi-class Sub-experts
We present a new type of multi-class learning algorithm called a linear-max algorithm. Linearmax algorithms learn with a special type of attribute called a sub-expert. A sub-exper...
Chris Mesterharm
ICML
2010
IEEE
15 years 6 months ago
Bayesian Multi-Task Reinforcement Learning
We consider the problem of multi-task reinforcement learning where the learner is provided with a set of tasks, for which only a small number of samples can be generated for any g...
Alessandro Lazaric, Mohammad Ghavamzadeh
ATAL
2005
Springer
15 years 10 months ago
Improving reinforcement learning function approximators via neuroevolution
Reinforcement learning problems are commonly tackled with temporal difference methods, which use dynamic programming and statistical sampling to estimate the long-term value of ta...
Shimon Whiteson
125
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ALT
1999
Springer
15 years 9 months ago
On the Uniform Learnability of Approximations to Non-Recursive Functions
Abstract. Blum and Blum (1975) showed that a class B of suitable recursive approximations to the halting problem is reliably EX-learnable. These investigations are carried on by sh...
Frank Stephan, Thomas Zeugmann
CIKM
2004
Springer
15 years 10 months ago
Hierarchical document categorization with support vector machines
Automatically categorizing documents into pre-defined topic hierarchies or taxonomies is a crucial step in knowledge and content management. Standard machine learning techniques ...
Lijuan Cai, Thomas Hofmann