By De-Shuang Huang, Kyungsook Han

ISBN-10: 3319220527

ISBN-13: 9783319220529

ISBN-10: 3319220535

ISBN-13: 9783319220536

This publication - at the side of 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 with regards to this subject matter have been specifically solicited, together with theories, methodologies, and functions in technology and know-how. This 12 months, the convention centred frequently on laptop studying concept and techniques, tender computing, photograph processing and desktop imaginative and prescient, wisdom discovery and knowledge mining, normal language processing and computational linguistics, clever keep watch over and automation, clever conversation networks and net purposes, bioinformatics concept and techniques, healthcare and clinical tools, and data security.

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

**Sample text**

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. With sparse technique, our algorithm is much more efﬁcient than the two EM algorithms of the MGP models, whereas the loss of accuracy is trivial compared with the increase on speed. The variational hard-cut EM algorithm makes a coarse conditionally independent assumption to the posterior and thus it is not as accurate as our proposed algorithm.

Wang and X. Yang m P i¼1 a2i , we also introduce the following operator Aa ¼ m X b 2 À aR b b R ÀR a þ ð 1 À a Þa þ ð1 À aÞah; i i i i i ð7Þ i¼1 D1 ¼ m X b2 À R b ÀR a : i i i ð8Þ i¼1 Deﬁnition 2. If k [ 0 meets & 0 D1 u À khu ¼ 0; n 2 X ; 0 u ¼ 0; n 2 @X ; we call k the principal weighted eigenvalue of operator D1 . Lemma 1. (see [9]) (Young inequality with e) For every e [ 0 and a; b ! 0, 1\p; q\1; 1p þ 1q ¼ 1, the following inequality holds eap eÀq=p bq þ : p q ab Proof. By the Young inequality, we can get ab ap bq þ ; p q hence b Þ ab ¼ ðe1=p aÞð 1 e =p ðe1=p aÞp ðeÀ1=p bÞq eap eÀq=p bq þ ¼ þ : p q p q 2 The Proof of Theorem 1 When X is bounded there is nothing to prove, we focus on the condition that X is unbounded.

Ma Prðzt ¼ cÞ ¼ pc ; c ¼ 1 $ C i:i:d for t ¼ 1 $ N; ð14Þ pðxt jzt ¼ cÞ $ Nðlc ; Sc Þ; c ¼ 1 $ C ; i:i:d for t ¼ 1 $ N: ð15Þ Furthermore, for each component, we use the FITC model due to its advantages mentioned in Sect. 3 to describe the SGP input-output relation h i pðYc jXc ; Uc ; hc Þ $ N 0; KðXc ; Uc jhc ÞKðUc ; Uc jhc ÞÀ1 KðUc ; Xc jhc Þ þ Kc þ r2c I ; ð16Þ where Uc denotes the pseudo inputs in the c-th component, h i Kc ¼ diag KðXc ; Xc jhc Þ À KðXc ; Uc jhc ÞKðUc ; Uc jhc ÞÀ1 KðUc ; Xc jhc Þ ; ð17Þ and Xc , Yc , hc follow the meanings in Sect.

### 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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