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Algorithmic Learning Theory

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Cover of 'Algorithmic Learning Theory'

Table of Contents

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    Book Overview
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    Chapter 1 A Vector-Contraction Inequality for Rademacher Complexities
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    Chapter 2 Localization of VC Classes: Beyond Local Rademacher Complexities
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    Chapter 3 Labeled Compression Schemes for Extremal Classes
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    Chapter 4 On Version Space Compression
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    Chapter 5 Learning with Rejection
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    Chapter 6 Sparse Learning for Large-Scale and High-Dimensional Data: A Randomized Convex-Concave Optimization Approach
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    Chapter 7 On the Evolution of Monotone Conjunctions: Drilling for Best Approximations
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    Chapter 8 Exact Learning of Juntas from Membership Queries
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    Chapter 9 Submodular Learning and Covering with Response-Dependent Costs
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    Chapter 10 Classifying the Arithmetical Complexity of Teaching Models
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    Chapter 11 Learning Finite Variants of Single Languages from Informant
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    Chapter 12 Intrinsic Complexity of Partial Learning
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    Chapter 13 Learning Pattern Languages over Groups
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    Chapter 14 The Maximum Cosine Framework for Deriving Perceptron Based Linear Classifiers
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    Chapter 15 Structural Online Learning
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    Chapter 16 An Upper Bound for Aggregating Algorithm for Regression with Changing Dependencies
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    Chapter 17 Algorithmic Learning Theory
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    Chapter 18 On Minimaxity of Follow the Leader Strategy in the Stochastic Setting
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    Chapter 19 A Combinatorial Metrical Task System Problem Under the Uniform Metric
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    Chapter 20 Competitive Portfolio Selection Using Stochastic Predictions
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    Chapter 21 Q( $$\lambda $$ ) with Off-Policy Corrections
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    Chapter 22 On the Prior Sensitivity of Thompson Sampling
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    Chapter 23 Finding Meaningful Cluster Structure Amidst Background Noise
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    Chapter 24 A Spectral Algorithm with Additive Clustering for the Recovery of Overlapping Communities in Networks
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Title
Algorithmic Learning Theory
Published by
Springer International Publishing, October 2016
DOI 10.1007/978-3-319-46379-7
ISBNs
978-3-31-946378-0, 978-3-31-946379-7
Editors

Ortner, Ronald, Simon, Hans Ulrich, Zilles, Sandra

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 9 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Korea, Republic of 1 11%
Unknown 8 89%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 3 33%
Other 2 22%
Student > Master 1 11%
Student > Bachelor 1 11%
Researcher 1 11%
Other 1 11%
Readers by discipline Count As %
Computer Science 3 33%
Economics, Econometrics and Finance 3 33%
Linguistics 1 11%
Unspecified 1 11%
Engineering 1 11%
Other 0 0%