Who Cited It

Hierarchical Mixtures of Experts and the EM Algorithm

1994 · Neural Computation · 2,684 citations · 9 from inside this corpus

Michael I. Jordan, Robert A. Jacobs

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.

Hierarchical Mixtures of Experts and the EM Algorithm (1994)Hierarchical Mixtures of Expe…Multilayer feedforward networks are universal approximators (1989)Multilayer feedforward networ…Statistical Analysis With Missing Data (1989)Statistical Analysis With Mis…Induction of Decision Trees (1986)Induction of Decision TreesInduction of decision trees (1986)Induction of decision treesMultilayer feedforward networks are universal approximators (1989)Multilayer feedforward networ…Generalized Additive Models. (1991)Generalized Additive Models.Multivariate Adaptive Regression Splines (1991)Multivariate Adaptive Regress…Adaptive Mixtures of Local Experts (1991)Adaptive Mixtures of Local Ex…A general regression neural network (1991)A general regression neural n…Multivariate Density Estimation (1992)Multivariate Density Estimati…Neural Networks and the Bias/Variance Dilemma (1992)Neural Networks and the Bias/…Statistical Analysis of Finite Mixture Distributions. (1987)Statistical Analysis of Finit…Mixture Densities, Maximum Likelihood and the EM Algorithm (1984)Mixture Densities, Maximum Li…The Analysis of Binary Data. (1971)The Analysis of Binary Data.Auto-association by multilayer perceptrons and singular value decomposition (1988)Auto-association by multilaye…Multitask Learning (1997)Multitask LearningEnsemble based systems in decision making (2006)Ensemble based systems in dec…Universal Approximation using Incremental Constructive Feedforward Networks with Random H… (2006)Universal Approximation using…Dynamic bayesian networks: representation, inference and learning (2002)Dynamic bayesian networks: re…A gentle tutorial of the em algorithm and its application to parameter estimation for Gau… (1998)A gentle tutorial of the em a…Ensembling neural networks: Many could be better than all (2002)Ensembling neural networks: M…Mixtures of Probabilistic Principal Component Analyzers (1999)Mixtures of Probabilistic Pri…Attribute-Based Classification for Zero-Shot Visual Object Categorization (2013)Attribute-Based Classificatio…Regularized multi--task learning (2004)Regularized multi--task learn…
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What this paper cites, inside the corpus

PaperYearCited
Multilayer feedforward networks are universal approximators198921,633
Statistical Analysis With Missing Data198917,507
Induction of Decision Trees198615,020
Induction of decision trees198612,427
Multilayer feedforward networks are universal approximators19899,357
Generalized Additive Models.19918,296
Multivariate Adaptive Regression Splines19918,114
Adaptive Mixtures of Local Experts19915,234
A general regression neural network19914,504
Multivariate Density Estimation19923,718
Neural Networks and the Bias/Variance Dilemma19923,612
Statistical Analysis of Finite Mixture Distributions.19872,951
Mixture Densities, Maximum Likelihood and the EM Algorithm19842,720
The Analysis of Binary Data.19712,289
Auto-association by multilayer perceptrons and singular value decomposition19881,369

What cites it, inside the corpus

PaperYearCited
Multitask Learning19976,478
Ensemble based systems in decision making20062,965
Universal Approximation using Incremental Constructive Feedforward Networks with Random H…20062,636
Dynamic bayesian networks: representation, inference and learning20022,604
A gentle tutorial of the em algorithm and its application to parameter estimation for Gau…19982,510
Ensembling neural networks: Many could be better than all20021,990
Mixtures of Probabilistic Principal Component Analyzers19991,906
Attribute-Based Classification for Zero-Shot Visual Object Categorization20131,674
Regularized multi--task learning20041,481

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Topics

Neural Networks and ApplicationsComputer Science
Bayesian Methods and Mixture ModelsComputer Science
Gaussian Processes and Bayesian InferenceComputer Science

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  • supports2 author record(s) attached.
  • supports36 reference(s) recorded.
  • supportsThe DOI's year agrees with the publication year.
  • supportsA title is present.

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Everything above was read from one stored OpenAlex payload, fetched 2026-09-04T03:58:48+00:00.

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