Download Advanced Intelligent Computing Theories and Applications: by De-Shuang Huang, Kyungsook Han PDF

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.

Show description

Read or Download Advanced Intelligent Computing Theories and Applications: 11th International Conference, ICIC 2015, Fuzhou, China, August 20-23, 2015. Proceedings, Part III PDF

Best machine theory books

Topology and Category Theory in Computer Science

This quantity displays the turning out to be use of suggestions from topology and class idea within the box of theoretical machine technological know-how. In so doing it bargains a resource of latest issues of a realistic taste whereas stimulating unique rules and options. Reflecting the most recent ideas on the interface among arithmetic and computing device technology, the paintings will curiosity researchers and complicated scholars in either fields.

Algorithmics : the spirit of computing

The easiest promoting 'Algorithmics' offers an important, thoughts, tools and effects which are basic to the technology of computing.  It starts off by means of introducing the fundamental principles of algorithms, together with their constructions and strategies of knowledge manipulation.  It then is going directly to exhibit the right way to layout actual and effective algorithms, and discusses their inherent boundaries.

R Data Mining Projects

Know about facts mining with real-world datasetsAbout This BookDiverse real-world datasets to coach facts mining techniquesPractical and all for real-world information mining circumstances, this publication covers ideas equivalent to spatial information mining, textual content mining, social media mining, and internet miningReal-world case reports illustrate a number of information mining innovations, taking you from amateur to intermediateWho This publication Is ForData analysts from newbie to intermediate point who want a step by step assisting hand in constructing advanced info mining tasks are the appropriate viewers for this ebook.

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

Sample text

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 [30]: " # 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 [34]. 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 ..

Download PDF sample

Rated 4.46 of 5 – based on 24 votes