Download Artificial Neural Networks in Pattern Recognition: 6th IAPR by Neamat El Gayar, Friedhelm Schwenker, Cheng Suen PDF

By Neamat El Gayar, Friedhelm Schwenker, Cheng Suen

This booklet constitutes the refereed lawsuits of the sixth IAPR TC3 overseas Workshop on synthetic Neural Networks in development reputation, ANNPR 2014, held in Montreal, quality controls, Canada, in October 2014. The 24 revised complete papers offered have been rigorously reviewed and chosen from 37 submissions for inclusion during this quantity. They disguise a wide range of subject matters within the box of studying algorithms and architectures and discussing the most recent examine, effects, and ideas in those areas.

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Extra info for Artificial Neural Networks in Pattern Recognition: 6th IAPR TC 3 International Workshop, ANNPR 2014, Montreal, QC, Canada, October 6-8, 2014. Proceedings

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For M training input-output pairs {xi , yi } (i = 1, . . , M ), the LS SVM is given by 1 C w w+ 2 2 minimize M ξi2 (2) i=1 yi f (xi ) = 1 − ξi subject to for i = 1, . . , M, (3) where C is the margin parameter, yi = 1 for Class 1 and −1 for Class 2, and ξi is the slack variable associated with xi . Multiplying yi to both sides of (3) and replacing yi ξi with ξi , we obtain C 1 w w+ 2 2 minimize subject to M ξi2 (4) i=1 f (xi ) = yi − ξi for i = 1, . . , M. (5) The above LS SVM is the same as the LS SVR.

6% . 0%. 7%. 6%. F. Abdel Hady et al. Table 4. 8%. 0%. 4 Discussion The improvement in recall means increase in the coverage of the trained NER model. This is attributed to the high quality of the training sentences selected by the proposed selective sampling criterion compared to random sampling. In addition, it is better than selecting target sentences where the English NER model is most confident about their corresponding English ones. The reason is that although the English NER model is most confident, this does not alleviate the passive nature of the target NER model as it has no control on the selection of its training data based on its performance.

Block Deletion Step 5 Delete temporarily feature i in I j and calculate Eijdel , where idel denotes that feature i is temporarily deleted. Step 6 Calculate S j . If S j is empty, I o = I j and go to Step 9. If only one feature is included in S j , set I j−1 = I j − S j , set j ← j − 1 and go to Step 5. If S j has more than two features, generate V j and go to Step 7. Step 7 Delete all the features in V j from I j : I j = I j − V j , where j = j − |V j | and |V j | denotes the number of elements in V j .

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