By De-Shuang Huang, Kyungsook Han
This publication - at the side of the double quantity LNCS 9225-9226 - constitutes the refereed court cases of the eleventh overseas convention on clever Computing, ICIC 2015, held in Fuzhou, China, in August 2015.
The eighty four papers of this quantity have been rigorously reviewed and chosen from 671 submissions. unique contributions relating to this topic have been in particular solicited, together with theories, methodologies, and functions in technological know-how and expertise. This yr, the convention targeted almost always on computer studying idea and strategies, smooth computing, snapshot processing and machine imaginative and prescient, wisdom discovery and information mining, average language processing and computational linguistics, clever regulate and automation, clever verbal exchange networks and net functions, bioinformatics idea and strategies, healthcare and scientific tools, and knowledge security.
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Extra info for Advanced Intelligent Computing Theories and Applications: 11th International Conference, ICIC 2015, Fuzhou, China, August 20-23, 2015. Proceedings, Part III
Learn. 45(2), 147–170 (2001) 9. : Quantifying the uncertainty in heritability. J. Hum. Genet. 59(5), 269–275 (2014) 10. : The Em algorithm for graphical association models with missing data. Comput. Stat. Data An. 19(2), 191–201 (1995) 11. : Short-term load forecasting using bayesian neural networks learned by Hybrid Monte Carlo algorithm. Appl. Soft Comput. 12(6), 1822–1827 (2012) 12. : Bayesian methods for neural networks and related models. Stat. Sci. 19 (1), 128–139 (2004) 13. : Articial Intelligence: A Modern Approach, p.
Nevertheless, GP model fails to describe multimodality dataset and the training of GP consumes O(N3) time for N training samples [5, 6]. In order to solve these problems, Tresp  proposed the Mixture of Gaussian Processes (MGP) in 2000, which was adjusted from Mixture of Experts. Since then, various MGP models have been proposed, and the most of them are special cases of mixture of experts where each expert is a GP. Another useful way of reducing the time cost of training a GP is to adopt the model of sparse Gaussian Process (SGP), which computes the GP likelihood with a pseudo dataset being smaller than the training dataset in size .
In: Proceedings of the 31st International Conference on Machine Learning, pp. 145–153 (2014) 10. : Sparse Gaussian processes for multi-task learning. In: The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, pp. 711–727 (2012) 11. : Variational inference for inﬁnite mixtures of Gaussian processes with applications to trafﬁc flow prediction. IEEE Trans. Intell. Transp. Syst. 12(2), 466–475 (2011) 12. : An alternative inﬁnite mixture of Gaussian process experts.
Advanced Intelligent Computing Theories and Applications: 11th International Conference, ICIC 2015, Fuzhou, China, August 20-23, 2015. Proceedings, Part III by De-Shuang Huang, Kyungsook Han