Lecture Notes in Computer Science - Algorithmic Learning Theory
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Published By Springer Berlin Heidelberg

9783642244117, 9783642244124

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Learning from Label Preferences

Lecture Notes in Computer Science - Algorithmic Learning Theory ◽

10.1007/978-3-642-24412-4_5 ◽

2011 ◽

pp. 38-38

Author(s):

Eyke Hüllermeier ◽

Johannes Fürnkranz

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The Perceptron with Dynamic Margin

Lecture Notes in Computer Science - Algorithmic Learning Theory ◽

10.1007/978-3-642-24412-4_18 ◽

2011 ◽

pp. 204-218 ◽

Cited By ~ 4

Author(s):

Constantinos Panagiotakopoulos ◽

Petroula Tsampouka

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Robust Learning of Automatic Classes of Languages

Lecture Notes in Computer Science - Algorithmic Learning Theory ◽

10.1007/978-3-642-24412-4_8 ◽

2011 ◽

pp. 55-69 ◽

Cited By ~ 1

Author(s):

Sanjay Jain ◽

Eric Martin ◽

Frank Stephan

Keyword(s):

Robust Learning

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Distributional Learning of Simple Context-Free Tree Grammars

Lecture Notes in Computer Science - Algorithmic Learning Theory ◽

10.1007/978-3-642-24412-4_31 ◽

2011 ◽

pp. 398-412 ◽

Cited By ~ 6

Author(s):

Anna Kasprzik ◽

Ryo Yoshinaka

Keyword(s):

Distributional Learning ◽

Free Tree ◽

Context Free

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Accelerated Training of Max-Margin Markov Networks with Kernels

Lecture Notes in Computer Science - Algorithmic Learning Theory ◽

10.1007/978-3-642-24412-4_24 ◽

2011 ◽

pp. 292-307

Author(s):

Xinhua Zhang ◽

Ankan Saha ◽

S. V. N. Vishwanathan

Keyword(s):

Markov Networks

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Domain Adaptation in Regression

Lecture Notes in Computer Science - Algorithmic Learning Theory ◽

10.1007/978-3-642-24412-4_25 ◽

2011 ◽

pp. 308-323 ◽

Cited By ~ 14

Author(s):

Corinna Cortes ◽

Mehryar Mohri

Keyword(s):

Domain Adaptation

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Approximate Reduction from AUC Maximization to 1-Norm Soft Margin Optimization

Lecture Notes in Computer Science - Algorithmic Learning Theory ◽

10.1007/978-3-642-24412-4_26 ◽

2011 ◽

pp. 324-337 ◽

Cited By ~ 2

Author(s):

Daiki Suehiro ◽

Kohei Hatano ◽

Eiji Takimoto

Keyword(s):

Auc Maximization ◽

Soft Margin

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On Upper-Confidence Bound Policies for Switching Bandit Problems

Lecture Notes in Computer Science - Algorithmic Learning Theory ◽

10.1007/978-3-642-24412-4_16 ◽

2011 ◽

pp. 174-188 ◽

Cited By ~ 66

Author(s):

Aurélien Garivier ◽

Eric Moulines

Keyword(s):

Confidence Bound ◽

Bandit Problems ◽

Upper Confidence Bound

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Iterative Learning from Positive Data and Counters

Lecture Notes in Computer Science - Algorithmic Learning Theory ◽

10.1007/978-3-642-24412-4_7 ◽

2011 ◽

pp. 40-54 ◽

Cited By ~ 3

Author(s):

Timo Kötzing

Keyword(s):

Iterative Learning ◽

Positive Data

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Models for Autonomously Motivated Exploration in Reinforcement Learning

Lecture Notes in Computer Science - Algorithmic Learning Theory ◽

10.1007/978-3-642-24412-4_2 ◽

2011 ◽

pp. 14-17

Author(s):

Peter Auer ◽

Shiau Hong Lim ◽

Chris Watkins

Keyword(s):

Reinforcement Learning

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Lecture Notes in Computer Science - Algorithmic Learning TheoryLatest Publications

TOTAL DOCUMENTS

H-INDEX

Published By Springer Berlin Heidelberg

Learning from Label Preferences

The Perceptron with Dynamic Margin

Robust Learning of Automatic Classes of Languages

Distributional Learning of Simple Context-Free Tree Grammars

Accelerated Training of Max-Margin Markov Networks with Kernels

Domain Adaptation in Regression

Approximate Reduction from AUC Maximization to 1-Norm Soft Margin Optimization

On Upper-Confidence Bound Policies for Switching Bandit Problems

Iterative Learning from Positive Data and Counters

Models for Autonomously Motivated Exploration in Reinforcement Learning

Lecture Notes in Computer Science - Algorithmic Learning Theory
Latest Publications