By De-Shuang Huang, Kyungsook Han
This ebook - at the side of the double quantity LNCS 9225-9226 - constitutes the refereed lawsuits 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 conscientiously reviewed and chosen from 671 submissions. unique contributions relating to this subject matter have been specifically solicited, together with theories, methodologies, and purposes in technology and expertise. This yr, the convention focused often on computer studying idea and techniques, gentle computing, photograph processing and desktop imaginative and prescient, wisdom discovery and knowledge mining, ordinary language processing and computational linguistics, clever regulate and automation, clever conversation networks and net functions, bioinformatics idea and strategies, healthcare and scientific tools, and knowledge security.
Read or Download Advanced Intelligent Computing Theories and Applications: 11th International Conference, ICIC 2015, Fuzhou, China, August 20-23, 2015. Proceedings, Part III PDF
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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
B6 .. 7; 6 6 .. 7 @4 . 5 4 4 . 5 . > > > > f Kðx mðx Þ > N N N ; x1 Þ > > : 2 yt $ Nðft ; r Þ Kðx1 ; x2 Þ ÁÁÁ Kðx2 ; x2 Þ .. ÁÁÁ .. KðxN ; x2 Þ Á Á Á Kðx1 ; xN Þ 31 C Kðx2 ; xN Þ 7 7C 7C .. 7C; 5A . KðxN ; xN Þ ð1Þ The Hard-Cut EM Algorithm for Mixture of Sparse 15 where xt, ft and yt denote the input, latent response and output of a training sample, respectively, K(u, v) is a mercer kernel function, and r2 denotes the noise intensity. As in most cases, we adopt zero mean function (m ≡ 0) and the most popular kernel function—the squared exponential (SE) kernel : " # d 1X 2 2 Kðu; vÞ ¼ l exp À b ðuk À vk Þ ; 2 k¼1 k 2 ð2Þ where d is the dimensionality of inputs and each dimension has a different weight bk to realize automatic feature selection.
3 On Kin40k Dataset Finally, we compare these algorithms on a popular real dataset called kin40k, which is generated by a robot arm simulator, with 10000 training samples, 30000 test samples and 9 attributes . The computational environment and implementation details remain the same as above. The mean and standard deviation of the predictive RMSEs as well as the training times for each algorithm are listed in Table 3. Similarly, for FITC and FITC mixture model, we initialize the kernel parameters by training a GP model on 500 randomly selected training samples before the MLE learning process.
The experimental results demonstrates that our proposed model and algorithm are feasible and can outperform some typical regression algorithms. 1 Gaussian Process (GP) A GP model for regression is mathematically deﬁned by 8 02 3 2 2 3 Kðx1 ; x1 Þ mðx1 Þ f1 > > > > B6 mðx2 Þ 7 6 Kðx2 ; x1 Þ 6f 7 > > B6 7 6 6 27 > < F ¼ 6 7 $ N B6 7 6 .. B6 .. 7; 6 6 .. 7 @4 . 5 4 4 . 5 . > > > > f Kðx mðx Þ > N N N ; x1 Þ > > : 2 yt $ Nðft ; r Þ Kðx1 ; x2 Þ ÁÁÁ Kðx2 ; x2 Þ .. ÁÁÁ .. KðxN ; x2 Þ Á Á Á Kðx1 ; xN Þ 31 C Kðx2 ; xN Þ 7 7C 7C ..