Read e-book online Analysis of Images, Social Networks and Texts: 4th PDF

By Mikhail Yu. Khachay, Natalia Konstantinova, Alexander Panchenko, Dmitry Ignatov, Valeri G. Labunets

ISBN-10: 3319261223

ISBN-13: 9783319261225

ISBN-10: 3319261231

ISBN-13: 9783319261232

This publication constitutes the court cases of the Fourth foreign convention on research of pictures, Social Networks and Texts, AIST 2015, held in Yekaterinburg, Russia, in April 2015.

The 24 complete and eight brief papers have been rigorously reviewed and chosen from a hundred and forty submissions. The papers are prepared in topical sections on research of pictures and movies; development popularity and computer studying; social community research; textual content mining and normal language processing.

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Read Online or Download Analysis of Images, Social Networks and Texts: 4th International Conference, AIST 2015, Yekaterinburg, Russia, April 9–11, 2015, Revised Selected Papers PDF

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Extra info for Analysis of Images, Social Networks and Texts: 4th International Conference, AIST 2015, Yekaterinburg, Russia, April 9–11, 2015, Revised Selected Papers

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46, 227–241 (2013) 16. : Pattern Recognition, 4th edn. , Amsterdam (2009) 17. : Face recognition from a single image per person: a survey. Pattern Recogn. 39(9), 1725–1745 (2006) 18. : DeepFace: closing the gap to human-level performance in face verification. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2014), pp. 1701–1708 (2014) 19. : Nonlinear transformation of the distance function in the nearest neighbor image recognition. S. ) CompIMAGE 2014. LNCS, vol. 8641, pp.

Ru 2 Data Mining Labs, St. org 3 Deloitte Analytics Institute, Moscow, Russia 4 Technical University Munich, Garching, Germany 5 Laboratory for Internet Studies, National Research University Higher School of Economics, St. ru 6 Steklov Mathematical Institute at St. Petersburg, St. com Abstract. In online social networks, high level features of user behavior such as character traits can be predicted with data from user profiles and their connections. Recent publications use data from online social networks to detect people with depression propensity and diagnosis.

In particular, we perform stepwise feature elimination by Akaike Information Criterion (AIC) to the “transformed” model. We consider the backward stepwise regression, starting with the full model and proceeding backwards by sequentially removing variables that improve the model by being deleted, until no more deletions will improve the model. On our data the result of this procedure is a model with only two remaining variables: the logarithm of the number of groups and transitivity. We will refer to this model as the “selected” one.

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Analysis of Images, Social Networks and Texts: 4th International Conference, AIST 2015, Yekaterinburg, Russia, April 9–11, 2015, Revised Selected Papers by Mikhail Yu. Khachay, Natalia Konstantinova, Alexander Panchenko, Dmitry Ignatov, Valeri G. Labunets


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