References
- Cho JH, Son H II, Lee DG. Gain-scheduling control of teleoperation systems interacting with soft tissues. IEEE Trans Indus Elect. 2013;60:946–957.10.1109/TIE.2012.2189537
- Wai R-J, Chuang K-L, Lee J-D. On-line supervisory control design for Maglev transportation system via total sliding-mode approach and particle swarm optimization. IEEE Trans Autom Cont. 2010;55:1544–1599.
- Ding Y-S, Liu B. An intelligent bi-cooperative decoupling control approach based on modulation mechanism of internal environment in body. IEEE Trans Cont Syst Technol. 2011;17:692–698.10.1109/TCST.2010.2047944
- Hofmann T. Unsupervised learning by probabilistic latent semantic analysis. Mach Learn. 2001;42:177–196.10.1023/A:1007617005950
- Blei DM, Ng AY, Jordan MI. Latent dirichlet allocation. J Mach Learn Res. 2003;3:993–1022.
- Casella G, George EI. Explaining the Gibbs sampler. Am Stat. 1992;46:167–174.
- Griffiths TL, Steyvers M. Finding scientific topics. Proc Nat Acad Sci USA. 2004;101:5228–5235.10.1073/pnas.0307752101
- Andrieu C, de Freitas N, Doucet A, et al. An introduction to MCMC for machine learning. Mach Learn. 2003;50:5–43.10.1023/A:1020281327116
- Blei DM, Jordan MI. Variational inference for Dirichlet process mixtures. Bayesian Anal. 2006;1:121–143.10.1214/06-BA104
- Teh YW, Newman D, Welling M. A collapsed variational Bayesian inference algorithm for latent Dirichlet allocation. Adv Neural Inf Proc Syst. 2007;19:1353–1360.
- Rosen-Zvi M, Chemudugunta C, Griffiths T, et al. Learning author-topic models from text corpora. ACM Trans Inf Syst (TOIS). 2010;28:4.
- Steyvers M, Smyth P, Rosen-Zvi M, et al. Probabilistic author-topic models for information discovery. In: Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining; Seattle, Washington, USA; 2004. p. 306–315.
- Porteous I, Newman D, Ihler A, et al. Fast collapsed Gibbs sampling for latent Dirichlet allocation. In: KDD’08: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; 2008 Aug 24–27; Las Vegas, NV; New York, NY: Association for Computing Machinery; p. 569–577.10.1145/1401890
- Asuncion A, Welling M, Smyth P, et al. The on smoothing and inference for topic models. In: 25th Conference on Uncertainty in Artificial Intelligence; UAI, Montreal, QC, Canada, Jun 18–21; AUAI Press, Arlington. 2009. p. 27–34.
- Wang X, McCallum A. Topics over time: a non-Markov continuous-time model of topical trends. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; 2006Aug 20–23; Philadelphia (PA): Association for Computing Machinery; p. 424–433.10.1145/1150402
- Wang X, McCallum A, Wei X. Topical n-grams: phrase and topic discovery, with an application to information retrieval. In: 7th IEEE International Conference on Data Mining (ICDM ‘07), Omaha (NE); 2007 Oct 28–31; IEEE: Piscataway. p. 697–702.
- Sun S, Feng Y, Dong C, et al. Efficient SRAM failure rate prediction via Gibbs sampling. IEEE Trans Comp Aided Des Integr Circuits Syst. 2012;31:1831–1844.10.1109/TCAD.2012.2209884