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Download Artificial Neural Networks in Pattern Recognition: 7th IAPR by Friedhelm Schwenker, Hazem M. Abbas, Neamat El Gayar, PDF

By Friedhelm Schwenker, Hazem M. Abbas, Neamat El Gayar, Edmondo Trentin

This ebook constitutes the refereed lawsuits of the seventh IAPR TC3 foreign Workshop on man made Neural Networks in development acceptance, ANNPR 2016, held in Ulm, Germany, in September 2016.

The 25 revised complete papers awarded including 2 invited papers have been conscientiously reviewed and chosen from 32 submissions for inclusion during this quantity. The workshop will act as an incredible discussion board for foreign researchers and practitioners operating in all parts of neural community- and laptop learning-based trend reputation to give and talk about the newest examine, effects, and concepts in those areas.

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Read Online or Download Artificial Neural Networks in Pattern Recognition: 7th IAPR TC3 Workshop, ANNPR 2016, Ulm, Germany, September 28–30, 2016, Proceedings PDF

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Additional resources for Artificial Neural Networks in Pattern Recognition: 7th IAPR TC3 Workshop, ANNPR 2016, Ulm, Germany, September 28–30, 2016, Proceedings

Example text

Improving Generalization Abilities of Maximal Average Margin Classifiers 37 Table 1. 2 Inputs Train Test Sets Banana 2 400 4, 900 100 Breast cancer 9 200 77 100 Diabetes 8 468 300 100 Flare-solar 9 666 400 100 German 20 700 300 100 Heart 13 170 Image 18 1, 300 Ringnorm 20 400 Splice 60 1, 000 Thyroid 5 140 75 100 Titanic 3 150 2, 051 100 Twonorm 20 400 7, 000 100 Waveform 21 400 4, 600 100 100 100 1, 010 20 7, 000 100 2, 175 20 Results Table 2 shows the average accuracies and their standard deviations of the six classifiers with RBF kernels.

1111,. . , 20}. 1, 1, 10, 50, 100, 500, 1000, 2000}. We trained the L1 SVM using SMO-NM [24], which fuses SMO (Sequential minimal optimization) and NM (Newton’s method). Improving Generalization Abilities of Maximal Average Margin Classifiers 37 Table 1. 2 Inputs Train Test Sets Banana 2 400 4, 900 100 Breast cancer 9 200 77 100 Diabetes 8 468 300 100 Flare-solar 9 666 400 100 German 20 700 300 100 Heart 13 170 Image 18 1, 300 Ringnorm 20 400 Splice 60 1, 000 Thyroid 5 140 75 100 Titanic 3 150 2, 051 100 Twonorm 20 400 7, 000 100 Waveform 21 400 4, 600 100 100 100 1, 010 20 7, 000 100 2, 175 20 Results Table 2 shows the average accuracies and their standard deviations of the six classifiers with RBF kernels.

As this is a “proof of concept” investigation several combinations of neural structure, encoding method and class combination were tried. The best classification ac-curacy is listed in Table 1 which exceeded that if any of the two class tests. In the two class testing each dysrhythmia was tested against the normal data. Some dysrhythmias, for example re-entry, returned a 100 % accuracy level for each of the spike encoding methods. Other dysfunctions, for example ectopic pacemaker, re-turned mixed results for each encoding method which were much lower than the four class situation.

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