Get 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 e-book constitutes the lawsuits 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 conscientiously reviewed and chosen from one hundred forty submissions. The papers are geared up in topical sections on research of pictures and video clips; development reputation and desktop studying; social community research; textual content mining and ordinary 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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Additional resources 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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SIFT (Scale-Invariant Feature Transform) [2], HOG (Histograms of Oriented Gradients) [3], SURF (Speeded-Up Robust Features) [4], etc. However, though image recognition technology has reached a certain level of maturity [5], many researchers try to improve the recognition quality by exploiting the hierarchical processing of analyzed objects. Such processing is known to be one of the known characteristic of human intellect [6, 7]. Y. Khachay et al. ): AIST 2015, CCIS 542, pp. 14–23, 2015. , pyramids of features (in particular, PHOG (Pyramid HOG) descriptor [8]), hierarchical temporal memory [6], wavelet-analysis [9], deep convolution neural networks [10], etc.

The first result is that in order achieve higher AUC values with GLM one has to consider proper variable transformations. 84. The second result is that the most valuable features that characterize the depression propensity are the (log) number of communities and the user’s transitivity within his or her personal ego network, which are sufficient for propensity detection in our setting. e. local clustering coefficient). It is worth mentioning that not only age and sex but also the number of friends turned out to be statistically insignificant.

Springer, Heidelberg (2012) 7. : Transitivity in structural models of small groups. Comp. Group Stud. 2, 107–124 (1971) 8. : AUC: a statistically consistent and more discriminating measure than accuracy. In: Proceedings of the International Joint Conference on Artificial Intelligence 2003, pp. 519–526 (2003) 9. : Suicide ideation of individuals in online social networks. PLoS ONE 8(4), e62262 (2013). 1371%2Fjournal. 0062262 10. : Analysis of twitter users’ mood for prediction of gold and silver prices in the stock market.

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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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