Download Web-Age Information Management: WAIM 2016 International by Shaoxu Song, Yongxin Tong PDF

By Shaoxu Song, Yongxin Tong

This e-book constitutes the refereed complaints of three workshops of the seventeenth foreign convention on Web-Age info administration, WAIM 2016, held in Nanchang, China, in June 2016.

The 3 workshops have been as follows:

• The overseas Workshop on Spatiotemporal info administration and Mining for the net (SDMMW 2016)

• The foreign Workshop on Semi-structured massive information administration and purposes (SemiBDMA 2016).

• The foreign Workshop on cellular internet information Analytics (MWDA2016)

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Read Online or Download Web-Age Information Management: WAIM 2016 International Workshops, MWDA, SDMMW, and SemiBDMA, Nanchang, China, June 3-5, 2016, Revised Selected Papers PDF

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Additional resources for Web-Age Information Management: WAIM 2016 International Workshops, MWDA, SDMMW, and SemiBDMA, Nanchang, China, June 3-5, 2016, Revised Selected Papers

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PERVASIVE 2005. LNCS, vol. 3468, pp. 62–79. Springer, Heidelberg (2005) 24. : Using unlabeled data in a sparse coding framework for human activity recognition. Pervasive Mob. Comput. 15, 242–262 (2014) 25. : Extracting and composing robust features with denoising autoencoders. In: Proceedings of the 25th International Conference on Machine Learning, pp. 1096–1103. ACM Press, New York (2008) 26. : Reducing the dimensionality of data with neural networks. Science 313, 504–507 (2006) 27. : A fast learning algorithm for deep belief nets.

Quant. Financ. 15(6), 999–1012 (2015) 12. : Mercer’s theorem, feature maps, and smoothing. U. ) COLT 2006. LNCS (LNAI), vol. 4005, pp. 154– 168. Springer, Heidelberg (2006). 1007/11776420 14 13. : Sparsification of linear models for large-scale text classification. In: Conf´erence sur l’APprentissage automatique (CAp 2015) (2015) 14. : Improving multi-class text classification method combined the svm classifier with oao and ddag strategies. J. Convergence Inf. Technol. 10(2), 62–70 (2015) 15. : Learning to classify short and sparse text & web with hidden topics from large-scale data collections.

This indicates that there exist underlying relations among these features. Also, instance-based learning method KNN also failed to give good results, and consistently obtains the lowest time-slice accu‐ racy and class accuracy. From Table 3, we can observe that deep learning based approaches outperformed their competing methods in time-slice accuracy. Although they failed to achieve the best class accuracy, their difference is quite small. 51 % of SVM. Similar conclusions can be drawn from Table 4.

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