Lecture Notes in Computer Science - Algorithmic Learning Theory
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30

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Published By Springer Berlin Heidelberg

9783540466499, 9783540466505

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Solving Semi-infinite Linear Programs Using Boosting-Like Methods

Lecture Notes in Computer Science - Algorithmic Learning Theory ◽

10.1007/11894841_2 ◽

2006 ◽

pp. 10-11

Author(s):

Gunnar Rätsch

Keyword(s):

Linear Programs ◽

Infinite Linear

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Learning Unions of ω(1)-Dimensional Rectangles

Lecture Notes in Computer Science - Algorithmic Learning Theory ◽

10.1007/11894841_7 ◽

2006 ◽

pp. 32-47 ◽

Author(s):

Alp Atıcı ◽

Rocco A. Servedio

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Hannan Consistency in On-Line Learning in Case of Unbounded Losses Under Partial Monitoring

Lecture Notes in Computer Science - Algorithmic Learning Theory ◽

10.1007/11894841_20 ◽

2006 ◽

pp. 229-243 ◽

Author(s):

Chamy Allenberg ◽

Peter Auer ◽

László Györfi ◽

György Ottucsák

Keyword(s):

On Line ◽

On Line Learning

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On Exact Learning Halfspaces with Random Consistent Hypothesis Oracle

Lecture Notes in Computer Science - Algorithmic Learning Theory ◽

10.1007/11894841_8 ◽

2006 ◽

pp. 48-62

Author(s):

Nader H. Bshouty ◽

Ehab Wattad

Keyword(s):

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Probabilistic Generalization of Simple Grammars and Its Application to Reinforcement Learning

Lecture Notes in Computer Science - Algorithmic Learning Theory ◽

10.1007/11894841_28 ◽

2006 ◽

pp. 348-362 ◽

Author(s):

Takeshi Shibata ◽

Ryo Yoshinaka ◽

Takashi Chikayama

Keyword(s):

Reinforcement Learning ◽

Probabilistic Generalization

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How Many Query Superpositions Are Needed to Learn?

Lecture Notes in Computer Science - Algorithmic Learning Theory ◽

10.1007/11894841_10 ◽

2006 ◽

pp. 78-92 ◽

Author(s):

Jorge Castro

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Leading Strategies in Competitive On-Line Prediction

Lecture Notes in Computer Science - Algorithmic Learning Theory ◽

10.1007/11894841_19 ◽

2006 ◽

pp. 214-228 ◽

Author(s):

Vladimir Vovk

Keyword(s):

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Unsupervised Slow Subspace-Learning from Stationary Processes

Lecture Notes in Computer Science - Algorithmic Learning Theory ◽

10.1007/11894841_29 ◽

2006 ◽

pp. 363-377 ◽

Author(s):

Andreas Maurer

Keyword(s):

Subspace Learning ◽

Stationary Processes

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General Discounting Versus Average Reward

Lecture Notes in Computer Science - Algorithmic Learning Theory ◽

10.1007/11894841_21 ◽

2006 ◽

pp. 244-258 ◽

Author(s):

Marcus Hutter

Keyword(s):

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Data-Driven Discovery Using Probabilistic Hidden Variable Models

Lecture Notes in Computer Science - Algorithmic Learning Theory ◽

10.1007/11894841_5 ◽

2006 ◽

pp. 28-28

Author(s):

Padhraic Smyth

Keyword(s):

Data Driven ◽

Hidden Variable ◽

Hidden Variable Models

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