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*Jacobs, R.A.*,

**Hierarchical Mixture of experts and the EM Algorithm**,

*NeurComp(6)*, 1994, pp. 181-214.
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**Training products of experts by minimizing contrastive divergence**,

*NeurComp(14)*, No. 8, 2002, pp. 1771-1800.

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**Experts' boasting in trainable fusion rules**,

*PAMI(25)*, No. 9, September 2003, pp. 1178-1182.

IEEE Abstract.
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Experts can lead to biases in fusion rules if training of experts and
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**A regularized minimum cross-entropy algorithm on mixtures of experts
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*PRL(27)*, No. 9, July 2006, pp. 947-955.

Elsevier DOI Regularization theory; Model selection; Time series prediction; Curve detection
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*Haas, O.C.L.*,
*Mills, J.A.*,

**A mixture of experts committee machine to design compensators for
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*PR(39)*, No. 9, September 2006, pp. 1704-1714.

Elsevier DOI
**0606**

Committee machines; Neural networks; Fuzzy C-means; Compensators;
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*Abbas, A.*,
*Andreopoulos, Y.*,

**Biased Mixtures of Experts: Enabling Computer Vision Inference Under
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*IP(29)*, 2020, pp. 7656-7667.

IEEE DOI
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Mixtures of experts, constrained data transfer,
single shot object detection, single image super resolution,
realtime action classification
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*Haredasht, F.N.[Fateme Nateghi]*,
*Beigy, H.[Hamid]*,

**Supervised fuzzy partitioning**,

*PR(97)*, 2020, pp. 107013.

Elsevier DOI
**1910**

Supervised k-means, Centroid-based clustering,
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*Akarun, L.[Lale]*,

**Conditional information gain networks as sparse mixture of experts**,

*PR(120)*, 2021, pp. 108151.

Elsevier DOI
**2109**

Machine learning, Deep learning, Conditional deep learning
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IEEE DOI

Predictive models, Routing, Probabilistic logic, Prediction algorithms, Data models, Classification algorithms BibRef

*Bochinski, E.*,
*Jongebloed, R.*,
*Tok, M.*,
*Sikora, T.*,

**Regularized Gradient Descent Training of Steered Mixture of Experts
for Sparse Image Representation**,

*ICIP18*(3873-3877)

IEEE DOI
**1809**

Kernel, Training, Optimization, Logic gates, Task analysis,
Gaussian mixture model, Sparse Image Representation,
Denoising
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*Ranzato, M.[Marc'Aurelio]*,
*Szlam, A.[Arthur]*,

**Hard Mixtures of Experts for Large Scale Weakly Supervised Vision**,

*CVPR17*(5085-5093)

IEEE DOI
**1711**

Data models, Decoding, Logic gates, Predictive models, Standards, Training
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*Aljundi, R.[Rahaf]*,
*Chakravarty, P.[Punarjay]*,
*Tuytelaars, T.[Tinne]*,

**Expert Gate: Lifelong Learning with a Network of Experts**,

*CVPR17*(7120-7129)

IEEE DOI
**1711**

Data models, Load modeling, Logic gates, Neural networks, Training,
Training, data
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*Seetharaman, G.*,

**Combining the advice of experts with randomized boosting for robust
pattern recognition**,

*AIPR13*(1-7)

IEEE DOI
**1408**

decision making
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*Yuksel, S.E.[Seniha Esen]*,
*Gader, P.D.[Paul D.]*,

**Variational Mixture of Experts for Classification with Applications to
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*ICPR10*(2981-2984).

IEEE DOI
**1008**

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*Principe, J.C.[Jose C.]*,

**Soft Competitive Principal Component Analysis Using
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*DARPA97*(1071-1076).
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**9700**

Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in

Hierarchical Combination, Multi-Stage Classifiers .

Last update:Jun 1, 2023 at 10:05:03