By Dominik Slezak, Gerald Schaefer, Son T. Vuong, Yoo-Sung Kim

ISBN-10: 3319099116

ISBN-13: 9783319099118

ISBN-10: 3319099124

ISBN-13: 9783319099125

This publication constitutes the lawsuits of the tenth overseas convention on energetic 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 offered have been rigorously reviewed and chosen from a number of submissions. the themes of those papers surround lively computers; interactive platforms and functions of AMT-based platforms; energetic media desktop studying and information mining suggestions; AMT for the semantic internet; social networks and cognitive foundations.

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Extra info for Active Media Technology: 10th International Conference, AMT 2014, Warsaw, Poland, August 11-14, 2014. Proceedings

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07.  160 150 G1 140 DB BB NC EM KS PR HITS LR FR FU 100 80 60 40 20 0 10 20 30 40 50 60 70 80 90 infected number 130 120  infected number 140 G2 120 110  180 100 90 80 70 100 10 20 30 (a) Time step 120 110 40 50 60 70 80 90 100 (b) Time step 3000 G3 100 G4 2500 Infected number Infected number 90 80 70 60 50 40 30 2000 1500 1000 500 20 10 0 10 20 30 40 50 60 70 80 90 0 100 200 (c) Time step 120 550 G6 500 100 450 90 400 80 70 60 50 40 30 800 1000 1200 1400 G7 350 300 250 200 150 100 20 50 10 0 600 (d) Time step Infected number Infected number 110 400 10 20 30 40 50 60 (e) Time step 70 80 90 100 0 50 100 150 200 250 300 350 400 450 500 550 600 (f) Time step Fig.

2608 Secondly, we compare our local (k-NN) regression transfer method with the non-transfer method, global transfer method, and KMM transfer method, respectively. We choose MARS method and LWR method as a part of local and global transfer methods. For MARS method, we can see that k-NN regression transfer method(transfer LMARS) performs better than non-transfer method, especially, it reduced the MSE about 30% in trait A, C and E. For LWR methods, k-NN regression transfer method(transfer LLWR) is better than non-transfer LWR method on A, C,and E trait, and is almost as well as non-transfer method on N and O traits.

The global transfer method is also a K-NN regression transfer learning method, and it takes all data points as neighbors. We also chose a transfer learning method called Kernel Mean Matching method(KMM) [5] as a baseline method, and tuned its parameters to make it work under a level of optimization. Further more, we integrated these importance reweighting techniques with some common weighted risk learning methods. In details, we combined clustering transfer method with Support Vector Machine(SVM) and Regression Tree method (RT) which both are very common regression methods, while we combined k-NN transfer method with MARS and LWR which can be integrated naturally and simply.

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Active Media Technology: 10th International Conference, AMT 2014, Warsaw, Poland, August 11-14, 2014. Proceedings by Dominik Slezak, Gerald Schaefer, Son T. Vuong, Yoo-Sung Kim


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