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Learning Theory and Kernel Machines

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Cover of 'Learning Theory and Kernel Machines'

Table of Contents

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    Book Overview
  2. Altmetric Badge
    Chapter 1 Tutorial: Learning Topics in Game-Theoretic Decision Making
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    Chapter 2 A General Class of No-Regret Learning Algorithms and Game-Theoretic Equilibria
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    Chapter 3 Preference Elicitation and Query Learning
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    Chapter 4 Efficient Algorithms for Online Decision Problems
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    Chapter 5 Positive Definite Rational Kernels
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    Chapter 6 Bhattacharyya and Expected Likelihood Kernels
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    Chapter 7 Maximal Margin Classification for Metric Spaces
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    Chapter 8 Maximum Margin Algorithms with Boolean Kernels
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    Chapter 9 Knowledge-Based Nonlinear Kernel Classifiers
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    Chapter 10 Fast Kernels for Inexact String Matching
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    Chapter 11 On Graph Kernels: Hardness Results and Efficient Alternatives
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    Chapter 12 Kernels and Regularization on Graphs
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    Chapter 13 Data-Dependent Bounds for Multi-category Classification Based on Convex Losses
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    Chapter 14 Comparing Clusterings by the Variation of Information
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    Chapter 15 Multiplicative Updates for Large Margin Classifiers
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    Chapter 16 Simplified PAC-Bayesian Margin Bounds
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    Chapter 17 Sparse Kernel Partial Least Squares Regression
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    Chapter 18 Sparse Probability Regression by Label Partitioning
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    Chapter 19 Learning with Rigorous Support Vector Machines
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    Chapter 20 Robust Regression by Boosting the Median
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    Chapter 21 Boosting with Diverse Base Classifiers
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    Chapter 22 Reducing Kernel Matrix Diagonal Dominance Using Semi-definite Programming
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    Chapter 23 Optimal Rates of Aggregation
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    Chapter 24 Distance-Based Classification with Lipschitz Functions
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    Chapter 25 Random Subclass Bounds
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    Chapter 26 PAC-MDL Bounds
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    Chapter 27 Universal Well-Calibrated Algorithm for On-Line Classification
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    Chapter 28 Learning Probabilistic Linear-Threshold Classifiers via Selective Sampling
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    Chapter 29 Learning Algorithms for Enclosing Points in Bregmanian Spheres
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    Chapter 30 Internal Regret in On-Line Portfolio Selection
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    Chapter 31 Lower Bounds on the Sample Complexity of Exploration in the Multi-armed Bandit Problem
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    Chapter 32 Smooth ε -Insensitive Regression by Loss Symmetrization
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    Chapter 33 On Finding Large Conjunctive Clusters
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    Chapter 34 Learning Arithmetic Circuits via Partial Derivatives
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    Chapter 35 Using a Linear Fit to Determine Monotonicity Directions
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    Chapter 36 Generalization Bounds for Voting Classifiers Based on Sparsity and Clustering
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    Chapter 37 Sequence Prediction Based on Monotone Complexity
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    Chapter 38 How Many Strings Are Easy to Predict?
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    Chapter 39 Polynomial Certificates for Propositional Classes
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    Chapter 40 On-Line Learning with Imperfect Monitoring
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    Chapter 41 Exploiting Task Relatedness for Multiple Task Learning
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    Chapter 42 Approximate Equivalence of Markov Decision Processes
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    Chapter 43 An Information Theoretic Tradeoff between Complexity and Accuracy
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    Chapter 44 Learning Random Log-Depth Decision Trees under the Uniform Distribution
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    Chapter 45 Projective DNF Formulae and Their Revision
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    Chapter 46 Learning with Equivalence Constraints and the Relation to Multiclass Learning
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    Chapter 47 Tutorial: Machine Learning Methods in Natural Language Processing
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    Chapter 48 Learning from Uncertain Data
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    Chapter 49 Learning and Parsing Stochastic Unification-Based Grammars
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    Chapter 50 Generality’s Price
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    Chapter 51 On Learning to Coordinate
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    Chapter 52 Learning All Subfunctions of a Function
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    Chapter 53 When Is Small Beautiful?
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    Chapter 54 Learning a Function of r Relevant Variables
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    Chapter 55 Subspace Detection: A Robust Statistics Formulation
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    Chapter 56 How Fast Is k -Means?
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    Chapter 57 Universal Coding of Zipf Distributions
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    Chapter 58 An Open Problem Regarding the Convergence of Universal A Priori Probability
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    Chapter 59 Entropy Bounds for Restricted Convex Hulls
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    Chapter 60 Compressing to VC Dimension Many Points
Attention for Chapter 14: Comparing Clusterings by the Variation of Information
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Chapter title
Comparing Clusterings by the Variation of Information
Chapter number 14
Book title
Learning Theory and Kernel Machines
Published in
Lecture notes in computer science, February 2016
DOI 10.1007/978-3-540-45167-9_14
Book ISBNs
978-3-54-040720-1, 978-3-54-045167-9
Authors

Marina Meilă

Mendeley readers

The data shown below were compiled from readership statistics for 219 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 8 4%
Germany 6 3%
France 3 1%
Malaysia 2 <1%
Portugal 2 <1%
Brunei Darussalam 1 <1%
Ireland 1 <1%
Réunion 1 <1%
Austria 1 <1%
Other 7 3%
Unknown 187 85%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 75 34%
Researcher 39 18%
Student > Master 35 16%
Student > Bachelor 17 8%
Student > Doctoral Student 11 5%
Other 30 14%
Unknown 12 5%
Readers by discipline Count As %
Computer Science 90 41%
Engineering 25 11%
Agricultural and Biological Sciences 16 7%
Mathematics 10 5%
Biochemistry, Genetics and Molecular Biology 9 4%
Other 46 21%
Unknown 23 11%