By Dominik Slezak, Gerald Schaefer, Son T. Vuong, Yoo-Sung Kim
This e-book constitutes the complaints of the tenth foreign convention on lively Media expertise, AMT 2014, held in Warsaw, Poland, in August 2014, held as a part of the 2014 net Intelligence Congress, WIC 2014. The forty seven complete papers provided have been rigorously reviewed and chosen from a variety of submissions. the themes of those papers surround energetic computers; interactive platforms and purposes of AMT-based structures; energetic media computing device studying and knowledge mining suggestions; AMT for the semantic internet; social networks and cognitive foundations.
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Extra resources for Active Media Technology: 10th International Conference, AMT 2014, Warsaw, Poland, August 11-14, 2014. Proceedings
Pardoe et al.  proposed a list of regression transfer learning algorithms based on boosting technology, some of which were developed from TrAdaBoost by Dai et al. . The method needs to use some labeled test instances, and can’t work without labels. 1 Local Regression Transfer Learning The Problem We aim to transfer the model trained under one situation to another new situation and predict users’ personality. Before transfer learning, what we would do is to aﬃrm whether datasets in the diﬀerent situations follow the same distribution.
The final ranking result of FR is from Eq. (7). F R(vi ) = (1 − σ) +σ n l ω(vl , vi ) r l=1 F R(vl ) (7) ω(vl , vr ) r=1 where σ (0 < σ < 1) is the damping coefficient. F R(vi ) is the influential value of l node vi . l=1 ω(vl ,vi ) r r=1 is the sum of ratios between weight from vl to vi and the sum ω(vl ,vr ) of weight from vl to all its pointed nodes. , vl } and l is the r value of vi ’s indegree. , vr } and r is the value of vl ’s outdegree. ω(vl , vi ) represents the weight from node vl to node vi , denoted as Eq.
Then, the proposed method combines topological attributes and diffused attributes of a node together, to identify the influential nodes in existing networks. 3 Multiple Attribute Fusion Method In this section, we formulate two combined strategies of MAF. One is based on the attribute union (FU) as shown in Sec. 1 and the other is based on the attribute ranking (FR) as shown in Sec. 2. Two strategies combine various attributes of a node together based on compound mode, in order to provide a universal method for overcoming the limitation of network topologies.