By De-Shuang Huang, Kyungsook Han
This publication - along side 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 with regards to this topic have been specifically solicited, together with theories, methodologies, and purposes in technological know-how and know-how. This yr, the convention centred commonly on computing device studying thought and strategies, smooth computing, photograph processing and machine imaginative and prescient, wisdom discovery and knowledge mining, normal language processing and computational linguistics, clever keep watch over and automation, clever conversation networks and internet purposes, bioinformatics idea and techniques, healthcare and clinical 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
The mixture of Gaussian Processes (MGP) is a powerful and fast developed machine learning framework. In order to make its learning more efﬁcient, certain sparsity constraints have been adopted to form the mixture of sparse Gaussian Processes (MSGP). However, the existing MGP and MSGP models are rather complicated and their learning algorithms involve various approximation schemes. In this paper, we reﬁne the MSGP model and develop the hard-cut EM algorithm for MSGP from its original version for MGP.
In: Proceedings of the 31st International Conference on Machine Learning, pp. 145–153 (2014) 10. : Sparse Gaussian processes for multi-task learning. In: The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, pp. 711–727 (2012) 11. : Variational inference for inﬁnite mixtures of Gaussian processes with applications to trafﬁc flow prediction. IEEE Trans. Intell. Transp. Syst. 12(2), 466–475 (2011) 12. : An alternative inﬁnite mixture of Gaussian process experts.
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. In: Proceedings of the 15th International Conference on Artiﬁcial Intelligence and Statistics, JMLR:W&CP, vol.
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