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Algorithmic Learning Theory

Overview of attention for book
Cover of 'Algorithmic Learning Theory'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 A Vector-Contraction Inequality for Rademacher Complexities
  3. Altmetric Badge
    Chapter 2 Localization of VC Classes: Beyond Local Rademacher Complexities
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    Chapter 3 Labeled Compression Schemes for Extremal Classes
  5. Altmetric Badge
    Chapter 4 On Version Space Compression
  6. Altmetric Badge
    Chapter 5 Learning with Rejection
  7. Altmetric Badge
    Chapter 6 Sparse Learning for Large-Scale and High-Dimensional Data: A Randomized Convex-Concave Optimization Approach
  8. Altmetric Badge
    Chapter 7 On the Evolution of Monotone Conjunctions: Drilling for Best Approximations
  9. Altmetric Badge
    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
  13. Altmetric Badge
    Chapter 12 Intrinsic Complexity of Partial Learning
  14. Altmetric Badge
    Chapter 13 Learning Pattern Languages over Groups
  15. Altmetric Badge
    Chapter 14 The Maximum Cosine Framework for Deriving Perceptron Based Linear Classifiers
  16. Altmetric Badge
    Chapter 15 Structural Online Learning
  17. Altmetric Badge
    Chapter 16 An Upper Bound for Aggregating Algorithm for Regression with Changing Dependencies
  18. Altmetric Badge
    Chapter 17 Algorithmic Learning Theory
  19. Altmetric Badge
    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
  22. Altmetric Badge
    Chapter 21 Q( $$\lambda $$ ) with Off-Policy Corrections
  23. Altmetric Badge
    Chapter 22 On the Prior Sensitivity of Thompson Sampling
  24. Altmetric Badge
    Chapter 23 Finding Meaningful Cluster Structure Amidst Background Noise
  25. Altmetric Badge
    Chapter 24 A Spectral Algorithm with Additive Clustering for the Recovery of Overlapping Communities in Networks
Attention for Chapter 17: Algorithmic Learning Theory
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (73rd percentile)
  • Above-average Attention Score compared to outputs of the same age and source (59th percentile)

Mentioned by

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Citations

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1 Dimensions

Readers on

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16 Mendeley
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Chapter title
Algorithmic Learning Theory
Chapter number 17
Book title
Algorithmic Learning Theory
Published in
Lecture notes in computer science, January 2016
DOI 10.1007/978-3-319-46379-7_17
Book ISBNs
978-3-31-946378-0, 978-3-31-946379-7
Authors

Daniil Ryabko, Ryabko, Daniil

X Demographics

X Demographics

The data shown below were collected from the profiles of 10 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 16 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 13%
Unknown 14 88%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 7 44%
Researcher 6 38%
Professor 1 6%
Student > Master 1 6%
Student > Doctoral Student 1 6%
Other 0 0%
Readers by discipline Count As %
Computer Science 9 56%
Mathematics 2 13%
Physics and Astronomy 2 13%
Psychology 1 6%
Social Sciences 1 6%
Other 1 6%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 5. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 20 September 2022.
All research outputs
#7,328,542
of 25,712,965 outputs
Outputs from Lecture notes in computer science
#2,133
of 8,171 outputs
Outputs of similar age
#106,086
of 401,650 outputs
Outputs of similar age from Lecture notes in computer science
#237
of 582 outputs
Altmetric has tracked 25,712,965 research outputs across all sources so far. This one has received more attention than most of these and is in the 71st percentile.
So far Altmetric has tracked 8,171 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.2. This one has gotten more attention than average, scoring higher than 73% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 401,650 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 73% of its contemporaries.
We're also able to compare this research output to 582 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 59% of its contemporaries.