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

9783540729259, 9783540729273

Latest Documents Most Cited Documents Contributed Authors Related Sources Related Keywords

Prediction by Categorical Features: Generalization Properties and Application to Feature Ranking

Learning Theory - Lecture Notes in Computer Science ◽

10.1007/978-3-540-72927-3_40 ◽

2007 ◽

pp. 559-573

Author(s):

Sivan Sabato ◽

Shai Shalev-Shwartz

Keyword(s):

Feature Ranking

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Resource-Bounded Information Gathering for Correlation Clustering

Learning Theory - Lecture Notes in Computer Science ◽

10.1007/978-3-540-72927-3_46 ◽

2007 ◽

pp. 625-627 ◽

Cited By ~ 3

Author(s):

Pallika Kanani ◽

Andrew McCallum

Keyword(s):

Information Gathering ◽

Correlation Clustering

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Spectral Algorithms for Learning and Clustering

Learning Theory - Lecture Notes in Computer Science ◽

10.1007/978-3-540-72927-3_2 ◽

2007 ◽

pp. 3-4 ◽

Cited By ~ 1

Author(s):

Santosh S. Vempala

Keyword(s):

Spectral Algorithms

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Q-Learning with Linear Function Approximation

Learning Theory - Lecture Notes in Computer Science ◽

10.1007/978-3-540-72927-3_23 ◽

2007 ◽

pp. 308-322 ◽

Cited By ~ 18

Author(s):

Francisco S. Melo ◽

M. Isabel Ribeiro

Keyword(s):

Linear Function ◽

Function Approximation ◽

Q Learning ◽

Linear Function Approximation

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Suboptimality of Penalized Empirical Risk Minimization in Classification

Learning Theory - Lecture Notes in Computer Science ◽

10.1007/978-3-540-72927-3_12 ◽

2007 ◽

pp. 142-156 ◽

Cited By ~ 1

Author(s):

Guillaume Lecué

Keyword(s):

Empirical Risk Minimization ◽

Risk Minimization ◽

Empirical Risk

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Resampling-Based Confidence Regions and Multiple Tests for a Correlated Random Vector

Learning Theory - Lecture Notes in Computer Science ◽

10.1007/978-3-540-72927-3_11 ◽

2007 ◽

pp. 127-141

Author(s):

Sylvain Arlot ◽

Gilles Blanchard ◽

Étienne Roquain

Keyword(s):

Random Vector ◽

Confidence Regions ◽

Multiple Tests

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Observational Learning in Random Networks

Learning Theory - Lecture Notes in Computer Science ◽

10.1007/978-3-540-72927-3_41 ◽

2007 ◽

pp. 574-588 ◽

Cited By ~ 1

Author(s):

Julian Lorenz ◽

Martin Marciniszyn ◽

Angelika Steger

Keyword(s):

Observational Learning ◽

Random Networks

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ℓ1 Regularization in Infinite Dimensional Feature Spaces

Learning Theory - Lecture Notes in Computer Science ◽

10.1007/978-3-540-72927-3_39 ◽

2007 ◽

pp. 544-558 ◽

Cited By ~ 3

Author(s):

Saharon Rosset ◽

Grzegorz Swirszcz ◽

Nathan Srebro ◽

Ji Zhu

Keyword(s):

Infinite Dimensional ◽

Feature Spaces ◽

ℓ1 Regularization

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Minimax Bounds for Active Learning

Learning Theory - Lecture Notes in Computer Science ◽

10.1007/978-3-540-72927-3_3 ◽

2007 ◽

pp. 5-19 ◽

Cited By ~ 10

Author(s):

Rui M. Castro ◽

Robert D. Nowak

Keyword(s):

Active Learning ◽

Minimax Bounds

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Generalized SMO-Style Decomposition Algorithms

Learning Theory - Lecture Notes in Computer Science ◽

10.1007/978-3-540-72927-3_27 ◽

2007 ◽

pp. 365-377

Author(s):

Nikolas List

Keyword(s):

Decomposition Algorithms

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Learning Theory - Lecture Notes in Computer ScienceLatest Publications

TOTAL DOCUMENTS

H-INDEX

Published By Springer Berlin Heidelberg

Prediction by Categorical Features: Generalization Properties and Application to Feature Ranking

Resource-Bounded Information Gathering for Correlation Clustering

Spectral Algorithms for Learning and Clustering

Q-Learning with Linear Function Approximation

Suboptimality of Penalized Empirical Risk Minimization in Classification

Resampling-Based Confidence Regions and Multiple Tests for a Correlated Random Vector

Observational Learning in Random Networks

ℓ1 Regularization in Infinite Dimensional Feature Spaces

Minimax Bounds for Active Learning

Generalized SMO-Style Decomposition Algorithms

Learning Theory - Lecture Notes in Computer Science
Latest Publications