14.1.13.2.4 Mixture of Experts, Multiple Classifiers, Combining Classifiers

Chapter Contents (Back)
Classifier Combinations. Classifier Ensembles. Mixture of Experts.

Jordan, M.I., Jacobs, R.A.,
Hierarchical Mixture of experts and the EM Algorithm,
NeurComp(6), 1994, pp. 181-214. Combining results. BibRef 9400

Hinton, G.E.[Geoffrey E.],
Training products of experts by minimizing contrastive divergence,
NeurComp(14), No. 8, 2002, pp. 1771-1800.
DOI Link BibRef 0200

Raudys, S.J.[Sarunas J.],
Experts' boasting in trainable fusion rules,
PAMI(25), No. 9, September 2003, pp. 1178-1182.
IEEE Abstract. 0309
Experts can lead to biases in fusion rules if training of experts and fusion rules use the same data. BibRef

Lu, Z.W.[Zhi-Wu],
A regularized minimum cross-entropy algorithm on mixtures of experts for time series prediction and curve detection,
PRL(27), No. 9, July 2006, pp. 947-955.
Elsevier DOI Regularization theory; Model selection; Time series prediction; Curve detection 0605
BibRef

Goodband, J.H., Haas, O.C.L., Mills, J.A.,
A mixture of experts committee machine to design compensators for intensity modulated radiation therapy,
PR(39), No. 9, September 2006, pp. 1704-1714.
Elsevier DOI 0606
Committee machines; Neural networks; Fuzzy C-means; Compensators; Radiation therapy BibRef

Abbas, A., Andreopoulos, Y.,
Biased Mixtures of Experts: Enabling Computer Vision Inference Under Data Transfer Limitations,
IP(29), 2020, pp. 7656-7667.
IEEE DOI 2007
Mixtures of experts, constrained data transfer, single shot object detection, single image super resolution, realtime action classification BibRef

Ashtari, P.[Pooya], 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, Entropy-based regularization, Feature weighting, Mixtures of experts BibRef

Bicici, U.C.[Ufuk Can], 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 BibRef


Zhao, W.[Wenbo], Gao, Y.[Yang], Memon, S.A.[Shahan Ali], Raj, B.[Bhiksha], Singh, R.[Rita],
Hierarchical Routing Mixture of Experts,
ICPR21(7900-7906)
IEEE DOI 2105
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 BibRef

Gross, S., 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 BibRef

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 BibRef

Peng, J.[Jing], Seetharaman, G.,
Combining the advice of experts with randomized boosting for robust pattern recognition,
AIPR13(1-7)
IEEE DOI 1408
decision making BibRef

Yuksel, S.E.[Seniha Esen], Gader, P.D.[Paul D.],
Variational Mixture of Experts for Classification with Applications to Landmine Detection,
ICPR10(2981-2984).
IEEE DOI 1008
BibRef

Fancourt, C.L.[Craig L.], Principe, J.C.[Jose C.],
Soft Competitive Principal Component Analysis Using the Mixture of Experts,
DARPA97(1071-1076). BibRef 9700

Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Hierarchical Combination, Multi-Stage Classifiers .


Last update:Oct 20, 2021 at 09:45:26