By De-Shuang Huang, Kyungsook Han
This publication - along side the double quantity LNCS 9225-9226 - constitutes the refereed complaints 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 rigorously reviewed and chosen from 671 submissions. unique contributions relating to this topic have been particularly solicited, together with theories, methodologies, and functions in technological know-how and know-how. This 12 months, the convention targeted usually on laptop studying conception and techniques, tender computing, photo processing and laptop imaginative and prescient, wisdom discovery and knowledge mining, common language processing and computational linguistics, clever keep an eye on and automation, clever communique networks and net functions, bioinformatics idea and techniques, healthcare and clinical tools, and knowledge security.
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Extra resources for Advanced Intelligent Computing Theories and Applications: 11th International Conference, ICIC 2015, Fuzhou, China, August 20-23, 2015. Proceedings, Part III
41(2), 425–434 (2011) 4. : Gaussian processes in reinforcement learning. , Schölkopf, B. ) Advances in Neural Information Processing Systems, vol. 16, pp. 751–759. MIT Press, Cambridge (2003) 5. : Variational mixture of Gaussian process experts. In: Advances in Neural Information Processing Systems, vol. 21, pp. 1897–1904 (2008) 6. : Gas distribution modeling using sparse Gaussian process mixture models. In: Proceedings of Robotics: Science and Systems, pp. 310–317 (2008) 7. : Mixtures of Gaussian processes.
Ma Table 3. 0001 Still, on the large dataset, the SVM, the precise hard-cut EM algorithm and the LOOCV hard-cut EM algorithm of MGP are prohibitively slow and fail to converge. From Table 3, we can observe that on the kin40 k dataset, our proposed algorithm is more precise than the other algorithms due to fewer approximations. A possible reason why our algorithm consumes so much time is that on a real dataset that doesn’t come from a GP or MGP model, our algorithm needs much more iterations. Therefore, a further improvement can be made by preprocessing a real dataset so that it is more like a dataset simulated from a MGP.
Monte-Carlo methods are very flexible when other methods are not applicable. The larger the samples, the more accurate results; however, the computational complexity is exponential power of the number of instances of the sample. Gibbs sampling is the most typical one in Monte-Carlo methods. 4 The Gaussian Approximation The Gaussian approximation method also can handle large samples and get accurate results; and it has lower complexity than Monte Carlo. The basic idea is: for the large scale data, we can use multivariate Gaussian distribution to approximately simulate p (θs|D, S) ∝ p(D|θs, S), g(θs) = log (p(D|θs, S)p(θs|S)).