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» Machine Learning by Function Decomposition
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ICML
2003
IEEE
16 years 2 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 2 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 7 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
97
Voted
ALT
1999
Springer
15 years 5 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 7 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