Analysis of Images, Social Networks and Texts: 4th by Mikhail Yu. Khachay, Natalia Konstantinova, Alexander

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

This ebook 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 conscientiously reviewed and chosen from one hundred forty submissions. The papers are equipped in topical sections on research of pictures and movies; trend reputation and computer studying; social community research; textual content mining and usual language processing.

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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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2 and 4) [7, 8]. Unfortunately, the performance of such methods is usually insufficient for many practical applications. Hence, engineers have to apply more simple nonhierarchical methods. It seems that the improvement of performance of hierarchical methods is one of the most crucial tasks in this field. Thus, in this paper we introduced a hierarchical image recognition algorithm based on statistical approach and the Chow’s rule (6) with the estimate of the posterior probability (9) on the basis of the properties of the HT-PNN [15].

Removing redundant variables leads to better model design in the case of the selected model, which also means that both remaining variables represent different sources of variation. 4 Results We investigated the possibility to distinct users with depression propensity in the Russian online social network VKontakte based on demographic and structural features of their profiles. 87. The first result is that in order achieve higher AUC values with GLM one has to consider proper variable transformations.

In some cases (Fig. 4) our approach is even able to increase the recognition accuracy over the PHOG (4). Though the posterior probability (9) is estimated based on the HT-PNN, this expression can be used with other similarity measures. Really, expression (9) is very similar to the output of widely used probabilistic neural network [20]. For instance, in our experiment we have shown the possibility to combine our approach with the state-of-the-art Euclidean metric. 8 % higher in comparison with the PHOG.

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