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Items: 1 to 20 of 194

1.

A bound on modeling error in observable operator models and an associated learning algorithm.

Zhao MJ, Jaeger H, Thon M.

Neural Comput. 2009 Sep;21(9):2687-712. doi: 10.1162/neco.2009.01-08-687.

PMID:
19548805
2.

Making the error-controlling algorithm of observable operator models constructive.

Zhao MJ, Jaeger H, Thon M.

Neural Comput. 2009 Dec;21(12):3460-86. doi: 10.1162/neco.2009.10-08-878.

PMID:
19686070
3.

Norm-observable operator models.

Zhao MJ, Jaeger H.

Neural Comput. 2010 Jul;22(7):1927-59. doi: 10.1162/neco.2010.03-09-983.

PMID:
20141473
4.

Factorial hidden Markov models and the generalized backfitting algorithm.

Jacobs RA, Jiang W, Tanner MA.

Neural Comput. 2002 Oct;14(10):2415-37.

PMID:
12396569
5.

Variational learning for switching state-space models.

Ghahramani Z, Hinton GE.

Neural Comput. 2000 Apr;12(4):831-64.

PMID:
10770834
6.

Observable operator models for discrete stochastic time series.

Jaeger H.

Neural Comput. 2000 Jun;12(6):1371-98.

PMID:
10935718
7.

Online learning with hidden markov models.

Mongillo G, Deneve S.

Neural Comput. 2008 Jul;20(7):1706-16. doi: 10.1162/neco.2008.10-06-351.

PMID:
18254694
8.

Approximate learning algorithm in Boltzmann machines.

Yasuda M, Tanaka K.

Neural Comput. 2009 Nov;21(11):3130-78. doi: 10.1162/neco.2009.08-08-844.

PMID:
19686066
9.

Predicting the phosphorylation sites using hidden Markov models and machine learning methods.

Senawongse P, Dalby AR, Yang ZR.

J Chem Inf Model. 2005 Jul-Aug;45(4):1147-52.

PMID:
16045309
10.

Partially observable Markov decision processes and performance sensitivity analysis.

Li Y, Yin B, Xi H.

IEEE Trans Syst Man Cybern B Cybern. 2008 Dec;38(6):1645-51. doi: 10.1109/TSMCB.2008.927711.

PMID:
19022734
11.

A constraint-based evolutionary learning approach to the expectation maximization for optimal estimation of the hidden Markov model for speech signal modeling.

Huda S, Yearwood J, Togneri R.

IEEE Trans Syst Man Cybern B Cybern. 2009 Feb;39(1):182-97. doi: 10.1109/TSMCB.2008.2004051. Epub 2008 Dec 9.

PMID:
19068441
12.

Robust sequential data modeling using an outlier tolerant hidden Markov model.

Chatzis SP, Kosmopoulos DI, Varvarigou TA.

IEEE Trans Pattern Anal Mach Intell. 2009 Sep;31(9):1657-69. doi: 10.1109/TPAMI.2008.215.

PMID:
19574625
13.

A unifying review of linear gaussian models.

Roweis S, Ghahramani Z.

Neural Comput. 1999 Feb 15;11(2):305-45. Review.

PMID:
9950734
14.

A tutorial of techniques for improving standard Hidden Markov Model algorithms.

Golod D, Brown DG.

J Bioinform Comput Biol. 2009 Aug;7(4):737-54.

PMID:
19634201
15.

Representational power of restricted boltzmann machines and deep belief networks.

Le Roux N, Bengio Y.

Neural Comput. 2008 Jun;20(6):1631-49. doi: 10.1162/neco.2008.04-07-510.

PMID:
18254699
16.

Time series modeling by a regression approach based on a latent process.

Chamroukhi F, Samé A, Govaert G, Aknin P.

Neural Netw. 2009 Jul-Aug;22(5-6):593-602. doi: 10.1016/j.neunet.2009.06.040. Epub 2009 Jul 4.

PMID:
19616918
17.

A fast learning algorithm for deep belief nets.

Hinton GE, Osindero S, Teh YW.

Neural Comput. 2006 Jul;18(7):1527-54.

PMID:
16764513
18.

Hidden neural networks.

Krogh A, Riis SK.

Neural Comput. 1999 Feb 15;11(2):541-63.

PMID:
9950743
19.

Data classification with multilayer perceptrons using a generalized error function.

Silva LM, Marques de Sá J, Alexandre LA.

Neural Netw. 2008 Nov;21(9):1302-10. doi: 10.1016/j.neunet.2008.04.004. Epub 2008 May 7.

PMID:
18572384
20.

The functional localization of neural networks using genetic algorithms.

Tsukimoto H, Hatano H.

Neural Netw. 2003 Jan;16(1):55-67.

PMID:
12576106

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