Who Cited It

Understanding Machine Learning: From Theory To Algorithms

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

Shai Shalev‐Shwartz, Shai Ben-David

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.

Understanding Machine Learning: From Theory To Algorithms (2015)Understanding Machine Learnin…Random Forests (2001)Random ForestsThe Nature of Statistical Learning Theory (1995)The Nature of Statistical Lea…Support-Vector Networks (1995)Support-Vector NetworksStatistical Learning Theory (1999)Statistical Learning TheoryC4.5: Programs for Machine Learning (1992)C4.5: Programs for Machine Le…Classification and Regression Trees. (1986)Classification and Regression…A Fast Learning Algorithm for Deep Belief Nets (2006)A Fast Learning Algorithm for…Induction of Decision Trees (1986)Induction of Decision TreesNo free lunch theorems for optimization (1997)No free lunch theorems for op…Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data (2001)Conditional Random Fields: Pr…The perceptron: A probabilistic model for information storage and organization in the bra… (1958)The perceptron: A probabilist…A training algorithm for optimal margin classifiers (1992)A training algorithm for opti…Learning with Kernels (2001)Learning with KernelsMachine learning a probabilistic perspective (2012)Machine learning a probabilis…Nonlinear Component Analysis as a Kernel Eigenvalue Problem (1998)Nonlinear Component Analysis …Information theory, inference, and learning algorithms (2004)Information theory, inference…Probabilistic graphical models : principles and techniques (2009)Probabilistic graphical model…A unified architecture for natural language processing (2008)A unified architecture for na…Learning Deep Architectures for AI (2009)Learning Deep Architectures f…A Practical Guide to Support Vector Classication (2008)A Practical Guide to Support …An introduction to hidden Markov models (1986)An introduction to hidden Mar…An Introduction to Support Vector Machines (2000)An Introduction to Support Ve…The Strength of Weak Learnability (1990)The Strength of Weak Learnabi…A Probabilistic Theory of Pattern Recognition (1996)A Probabilistic Theory of Pat…Prediction, Learning, and Games (2006)Prediction, Learning, and Gam…Learning Internal Representations by Error Propagation (1988)Learning Internal Representat…Solving Multiclass Learning Problems via Error-Correcting Output Codes (1995)Solving Multiclass Learning P…Rademacher and Gaussian Complexities: Risk Bounds and Structural Results (2001)Rademacher and Gaussian Compl…The Weighted Majority Algorithm (1994)Discriminative training methods for hidden Markov models (2002)Discriminative training metho…Learnability and the Vapnik-Chervonenkis dimension (1989)Learnability and the Vapnik-C…On the algorithmic implementation of multiclass kernel-based vector machines (2002)On the algorithmic implementa…Support Vector Machines (2020)Support Vector MachinesLearning Quickly When Irrelevant Attributes Abound: A New Linear-Threshold Algorithm (1988)Learning Quickly When Irrelev…
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What this paper cites, inside the corpus

PaperYearCited
Random Forests2001131,109
The Nature of Statistical Learning Theory199539,409
Support-Vector Networks199533,914
Statistical Learning Theory199926,957
C4.5: Programs for Machine Learning199223,704
Classification and Regression Trees.198621,044
A Fast Learning Algorithm for Deep Belief Nets200616,540
Induction of Decision Trees198615,020
No free lunch theorems for optimization199714,234
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data200112,994
The perceptron: A probabilistic model for information storage and organization in the bra…195811,965
A training algorithm for optimal margin classifiers199211,664
Learning with Kernels20019,631
Machine learning a probabilistic perspective20129,324
Nonlinear Component Analysis as a Kernel Eigenvalue Problem19988,138
Information theory, inference, and learning algorithms20046,580
Probabilistic graphical models : principles and techniques20096,450
A unified architecture for natural language processing20085,207
Learning Deep Architectures for AI20095,077
A Practical Guide to Support Vector Classication20085,074
An introduction to hidden Markov models19864,812
An Introduction to Support Vector Machines20004,134
The Strength of Weak Learnability19903,344
A Probabilistic Theory of Pattern Recognition19963,327
Prediction, Learning, and Games20063,279
Learning Internal Representations by Error Propagation19883,036
Solving Multiclass Learning Problems via Error-Correcting Output Codes19952,729
Rademacher and Gaussian Complexities: Risk Bounds and Structural Results20012,126
The Weighted Majority Algorithm19942,034
Discriminative training methods for hidden Markov models20021,895
Learnability and the Vapnik-Chervonenkis dimension19891,855
On the algorithmic implementation of multiclass kernel-based vector machines20021,789
Support Vector Machines20201,484
Learning Quickly When Irrelevant Attributes Abound: A New Linear-Threshold Algorithm19881,382

Links

DOI · OpenAlex record

Topics

Machine Learning and AlgorithmsComputer Science
Neural Networks and ApplicationsComputer Science
Computability, Logic, AI AlgorithmsComputer Science

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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…