Advanced Intelligent Computing Theories and Applications: - download pdf or read online

By De-Shuang Huang, Kyungsook Han

ISBN-10: 3319220527

ISBN-13: 9783319220529

ISBN-10: 3319220535

ISBN-13: 9783319220536

This publication - together with the double quantity LNCS 9225-9226 - constitutes the refereed lawsuits of the eleventh foreign convention on clever Computing, ICIC 2015, held in Fuzhou, China, in August 2015.

The eighty four papers of this quantity have been conscientiously reviewed and chosen from 671 submissions. unique contributions regarding this subject have been specially solicited, together with theories, methodologies, and functions in technology and expertise. This 12 months, the convention focused more often than not on computer studying concept and strategies, smooth computing, picture processing and machine imaginative and prescient, wisdom discovery and information mining, typical language processing and computational linguistics, clever keep watch over and automation, clever verbal exchange networks and internet purposes, bioinformatics conception and strategies, healthcare and clinical tools, and data security.

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Additional resources for Advanced Intelligent Computing Theories and Applications: 11th International Conference, ICIC 2015, Fuzhou, China, August 20-23, 2015. Proceedings, Part III

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Keywords: Small-world network Global asymptotical stability Á SIRS model Á Saturated infection rate Á 1 Introduction One of the main struggles between human and disease is about the infectious disease. The spread of smallpox, plague, cholera, H1N1virus has being made people’s life suffering a serious threat. And complex network provides a new platform for the study of the spread of the virus in terms of network topology. , the computer network also has the characteristics of such small-world network.

For each of these algorithms, the mean and standard deviation of the root mean squared errors (RMSEs) for prediction and the training times are listed in Table 1. In addition, to make the prediction of FITC and its mixture model more accurate, we initialize the kernel parameters by training a GP model on 20 randomly selected training samples before the MLE learning process, as did in [8]. Table 1. 5132 It can be seen from Table 1 that on the synthetic dataset, our proposed algorithm is much more accurate than the single FITC since the dataset is multimodal.

3541, pp. 86–96. Springer, Heidelberg (2005) 24 Z. Chen and J. Ma 24. : Gaussian process segmentation of co-moving animals. Proc. Am. Inst. Phys. 1305(1), 430–437 (2011) 25. : Overlapping mixtures of Gaussian processes for the data association problem. Pattern Recogn. 45(4), 1386–1395 (2012) 26. : An efficient EM approach to parameter learning of the mixture of Gaussian processes. , He, H. ) ISNN 2011, Part II. LNCS, vol. 6676, pp. 165–174. Springer, Heidelberg (2011) 27. : Markov logic mixtures of Gaussian processes: towards machines reading regression data.

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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

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