Understanding Machine Learning: From Theory To Algorithms

Shai Shalev‐Shwartz; Shai Ben-David

doi:https://doi.org/10.1017/cbo9781107298019

Understanding Machine Learning: From Theory To Algorithms

2015 · 3,081 citations · 0 from inside this corpus

The source holds an abstract for this work, but its best open-access copy is under no open licence, which does not permit us to republish the text. Read it at the source below.

34 of 34 neighbouring works in this corpus. Blue is what this paper cites; orange is what cites it, and a dashed line is one neighbour citing another. Only the largest labels are drawn — every node carries its full title on hover.

this paper works it cites works citing it node size = global citations · hover for the full title

Citation edge evidence

Each row is directed from the citing work to the cited work. Source status remains readable without the interactive graph.

Understanding Machine Learning: From Theory To Algorithms→Random ForestsLegacy edge — source detail unavailable
Understanding Machine Learning: From Theory To Algorithms→The Nature of Statistical Learning TheoryLegacy edge — source detail unavailable
Understanding Machine Learning: From Theory To Algorithms→Support-Vector NetworksLegacy edge — source detail unavailable
Understanding Machine Learning: From Theory To Algorithms→Statistical Learning TheoryLegacy edge — source detail unavailable
Understanding Machine Learning: From Theory To Algorithms→C4.5: Programs for Machine LearningLegacy edge — source detail unavailable
Understanding Machine Learning: From Theory To Algorithms→Classification and Regression Trees.Legacy edge — source detail unavailable
Understanding Machine Learning: From Theory To Algorithms→A Fast Learning Algorithm for Deep Belief NetsLegacy edge — source detail unavailable
Understanding Machine Learning: From Theory To Algorithms→Induction of Decision TreesLegacy edge — source detail unavailable
Understanding Machine Learning: From Theory To Algorithms→No free lunch theorems for optimizationLegacy edge — source detail unavailable
Understanding Machine Learning: From Theory To Algorithms→Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence DataLegacy edge — source detail unavailable
Understanding Machine Learning: From Theory To Algorithms→The perceptron: A probabilistic model for information storage and organization in the bra…Legacy edge — source detail unavailable
Understanding Machine Learning: From Theory To Algorithms→A training algorithm for optimal margin classifiersLegacy edge — source detail unavailable
Understanding Machine Learning: From Theory To Algorithms→Learning with KernelsLegacy edge — source detail unavailable
Understanding Machine Learning: From Theory To Algorithms→Machine learning a probabilistic perspectiveLegacy edge — source detail unavailable
Understanding Machine Learning: From Theory To Algorithms→Nonlinear Component Analysis as a Kernel Eigenvalue ProblemLegacy edge — source detail unavailable
Understanding Machine Learning: From Theory To Algorithms→Information theory, inference, and learning algorithmsLegacy edge — source detail unavailable
Understanding Machine Learning: From Theory To Algorithms→Probabilistic graphical models : principles and techniquesLegacy edge — source detail unavailable
Understanding Machine Learning: From Theory To Algorithms→A unified architecture for natural language processingLegacy edge — source detail unavailable
Understanding Machine Learning: From Theory To Algorithms→Learning Deep Architectures for AILegacy edge — source detail unavailable
Understanding Machine Learning: From Theory To Algorithms→A Practical Guide to Support Vector ClassicationLegacy edge — source detail unavailable
Understanding Machine Learning: From Theory To Algorithms→An introduction to hidden Markov modelsLegacy edge — source detail unavailable
Understanding Machine Learning: From Theory To Algorithms→An Introduction to Support Vector MachinesLegacy edge — source detail unavailable
Understanding Machine Learning: From Theory To Algorithms→The Strength of Weak LearnabilityLegacy edge — source detail unavailable
Understanding Machine Learning: From Theory To Algorithms→A Probabilistic Theory of Pattern RecognitionLegacy edge — source detail unavailable
Understanding Machine Learning: From Theory To Algorithms→Prediction, Learning, and GamesLegacy edge — source detail unavailable
Understanding Machine Learning: From Theory To Algorithms→Learning Internal Representations by Error PropagationLegacy edge — source detail unavailable
Understanding Machine Learning: From Theory To Algorithms→Solving Multiclass Learning Problems via Error-Correcting Output CodesLegacy edge — source detail unavailable
Understanding Machine Learning: From Theory To Algorithms→Rademacher and Gaussian Complexities: Risk Bounds and Structural ResultsLegacy edge — source detail unavailable
Understanding Machine Learning: From Theory To Algorithms→The Weighted Majority AlgorithmLegacy edge — source detail unavailable
Understanding Machine Learning: From Theory To Algorithms→Discriminative training methods for hidden Markov modelsLegacy edge — source detail unavailable
Understanding Machine Learning: From Theory To Algorithms→Learnability and the Vapnik-Chervonenkis dimensionLegacy edge — source detail unavailable
Understanding Machine Learning: From Theory To Algorithms→On the algorithmic implementation of multiclass kernel-based vector machinesLegacy edge — source detail unavailable
Understanding Machine Learning: From Theory To Algorithms→Support Vector MachinesLegacy edge — source detail unavailable
Understanding Machine Learning: From Theory To Algorithms→Learning Quickly When Irrelevant Attributes Abound: A New Linear-Threshold AlgorithmLegacy edge — source detail unavailable
Prediction, Learning, and Games→An Introduction to Support Vector MachinesLegacy edge — source detail unavailable
Prediction, Learning, and Games→The perceptron: A probabilistic model for information storage and organization in the bra…Legacy edge — source detail unavailable
Prediction, Learning, and Games→A training algorithm for optimal margin classifiersLegacy edge — source detail unavailable
Prediction, Learning, and Games→The Weighted Majority AlgorithmLegacy edge — source detail unavailable
Prediction, Learning, and Games→Support-Vector NetworksLegacy edge — source detail unavailable
Prediction, Learning, and Games→Learning Quickly When Irrelevant Attributes Abound: A New Linear-Threshold AlgorithmLegacy edge — source detail unavailable
Prediction, Learning, and Games→Statistical Learning TheoryLegacy edge — source detail unavailable
Solving Multiclass Learning Problems via Error-Correcting Output Codes→The perceptron: A probabilistic model for information storage and organization in the bra…Legacy edge — source detail unavailable
Solving Multiclass Learning Problems via Error-Correcting Output Codes→The Strength of Weak LearnabilityLegacy edge — source detail unavailable
Solving Multiclass Learning Problems via Error-Correcting Output Codes→Learning Internal Representations by Error PropagationLegacy edge — source detail unavailable
Solving Multiclass Learning Problems via Error-Correcting Output Codes→Classification and Regression Trees.Legacy edge — source detail unavailable
Discriminative training methods for hidden Markov models→The perceptron: A probabilistic model for information storage and organization in the bra…Legacy edge — source detail unavailable
Discriminative training methods for hidden Markov models→Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence DataLegacy edge — source detail unavailable
Learning Deep Architectures for AI→A training algorithm for optimal margin classifiersLegacy edge — source detail unavailable
Learning Deep Architectures for AI→An introduction to hidden Markov modelsLegacy edge — source detail unavailable
Learning Deep Architectures for AI→A unified architecture for natural language processingLegacy edge — source detail unavailable
Learning Deep Architectures for AI→Support-Vector NetworksLegacy edge — source detail unavailable
Learning Deep Architectures for AI→A Fast Learning Algorithm for Deep Belief NetsLegacy edge — source detail unavailable
Learning Deep Architectures for AI→Nonlinear Component Analysis as a Kernel Eigenvalue ProblemLegacy edge — source detail unavailable
Learning Deep Architectures for AI→The Nature of Statistical Learning TheoryLegacy edge — source detail unavailable
Learning Deep Architectures for AI→Random ForestsLegacy edge — source detail unavailable
Learning Deep Architectures for AI→Classification and Regression Trees.Legacy edge — source detail unavailable
The Strength of Weak Learnability→Learnability and the Vapnik-Chervonenkis dimensionLegacy edge — source detail unavailable
A Practical Guide to Support Vector Classication→A training algorithm for optimal margin classifiersLegacy edge — source detail unavailable
A Practical Guide to Support Vector Classication→Support-Vector NetworksLegacy edge — source detail unavailable
A Practical Guide to Support Vector Classication→The Nature of Statistical Learning TheoryLegacy edge — source detail unavailable
Support-Vector Networks→A training algorithm for optimal margin classifiersLegacy edge — source detail unavailable
Support-Vector Networks→Learning Internal Representations by Error PropagationLegacy edge — source detail unavailable
Learning Quickly When Irrelevant Attributes Abound: A New Linear-Threshold Algorithm→Learnability and the Vapnik-Chervonenkis dimensionLegacy edge — source detail unavailable
Nonlinear Component Analysis as a Kernel Eigenvalue Problem→A training algorithm for optimal margin classifiersLegacy edge — source detail unavailable
Nonlinear Component Analysis as a Kernel Eigenvalue Problem→Support-Vector NetworksLegacy edge — source detail unavailable
Nonlinear Component Analysis as a Kernel Eigenvalue Problem→The Nature of Statistical Learning TheoryLegacy edge — source detail unavailable
Statistical Learning Theory→The Nature of Statistical Learning TheoryLegacy edge — source detail unavailable
Learnability and the Vapnik-Chervonenkis dimension→Learning Quickly When Irrelevant Attributes Abound: A New Linear-Threshold AlgorithmLegacy edge — source detail unavailable
On the algorithmic implementation of multiclass kernel-based vector machines→An Introduction to Support Vector MachinesLegacy edge — source detail unavailable
On the algorithmic implementation of multiclass kernel-based vector machines→Solving Multiclass Learning Problems via Error-Correcting Output CodesLegacy edge — source detail unavailable
On the algorithmic implementation of multiclass kernel-based vector machines→A training algorithm for optimal margin classifiersLegacy edge — source detail unavailable
On the algorithmic implementation of multiclass kernel-based vector machines→Support-Vector NetworksLegacy edge — source detail unavailable
On the algorithmic implementation of multiclass kernel-based vector machines→Statistical Learning TheoryLegacy edge — source detail unavailable
On the algorithmic implementation of multiclass kernel-based vector machines→Classification and Regression Trees.Legacy edge — source detail unavailable
Rademacher and Gaussian Complexities: Risk Bounds and Structural Results→A Probabilistic Theory of Pattern RecognitionLegacy edge — source detail unavailable

What this paper cites, inside the corpus

Paper	Year	Cited
Random Forests	2001	131,109
The Nature of Statistical Learning Theory	1995	39,409
Support-Vector Networks	1995	33,914
Statistical Learning Theory	1999	26,957
C4.5: Programs for Machine Learning	1992	23,704
Classification and Regression Trees.	1986	21,044
A Fast Learning Algorithm for Deep Belief Nets	2006	16,540
Induction of Decision Trees	1986	15,020
No free lunch theorems for optimization	1997	14,234
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data	2001	12,994
The perceptron: A probabilistic model for information storage and organization in the bra…	1958	11,965
A training algorithm for optimal margin classifiers	1992	11,664
Learning with Kernels	2001	9,631
Machine learning a probabilistic perspective	2012	9,324
Nonlinear Component Analysis as a Kernel Eigenvalue Problem	1998	8,138
Information theory, inference, and learning algorithms	2004	6,580
Probabilistic graphical models : principles and techniques	2009	6,450
A unified architecture for natural language processing	2008	5,207
Learning Deep Architectures for AI	2009	5,077
A Practical Guide to Support Vector Classication	2008	5,074
An introduction to hidden Markov models	1986	4,812
An Introduction to Support Vector Machines	2000	4,134
The Strength of Weak Learnability	1990	3,344
A Probabilistic Theory of Pattern Recognition	1996	3,327
Prediction, Learning, and Games	2006	3,279
Learning Internal Representations by Error Propagation	1988	3,036
Solving Multiclass Learning Problems via Error-Correcting Output Codes	1995	2,729
Rademacher and Gaussian Complexities: Risk Bounds and Structural Results	2001	2,126
The Weighted Majority Algorithm	1994	2,034
Discriminative training methods for hidden Markov models	2002	1,895
Learnability and the Vapnik-Chervonenkis dimension	1989	1,855
On the algorithmic implementation of multiclass kernel-based vector machines	2002	1,789
Support Vector Machines	2020	1,484
Learning Quickly When Irrelevant Attributes Abound: A New Linear-Threshold Algorithm	1988	1,382

Links

Download BibTeX · Download RIS · DOI · OpenAlex record

Topics

Machine Learning and Algorithms	Computer Science
Neural Networks and Applications	Computer Science
Computability, Logic, AI Algorithms	Computer Science

Is this record sound?

complete

Nothing in this record contradicts itself and no field we check is missing.

supports2 author record(s) attached.
supports192 reference(s) recorded.
neutralThe DOI carries no year to check against.
supportsA title is present.

Provenance

Everything above was read from one stored OpenAlex payload, fetched 2026-09-04T03:58:47+00:00.

sha256 a08467ae9504f237…