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

By Neamat El Gayar, Friedhelm Schwenker, Cheng Suen

This booklet constitutes the refereed lawsuits of the sixth IAPR TC3 foreign Workshop on man made Neural Networks in development popularity, 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 study, effects, and ideas in those areas.

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Additional resources 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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14) is not always satisfied. We iterate the above BABD for I j+iInc . Let the resulting set of features be o I , where o ≤ j + iInc . Then E o ≤ T m +iInc (15) Eo ≤ T m (16) is satisfied. If (14) is satisfied, is also satisfied. But otherwise, there is no guarantee that the above inequality is satisfied. 40 S. Abe If (16) is satisfied, we repeat BABD adding the variables not processed. Otherwise, we consider that the BABD for this step failed and undo the feature selection at this step; namely, we restart BABD with threshold T m and I j , and add remaining features to I j .

2. Projection of named-entity tags from English to Chinese and French sentences The score of each target sentence depends on the score given to its corresponding source sentence in the parallel corpus, as follows: score(S) = min max wi ∈S cj ∈classes P (cj |wi , θsrc ) (1) The source NER model θsrc assigns probability for each token of how likely it belongs to each entity type: person, location, organization or otherwise. Then, the entity type for each token is the class with maximum probability P (cj |wi , θsrc ).

Adaptive forward-backward greedy algorithm for sparse learning with linear models. In: NIPS 21, pp. 1921–1928 (2009) 12. : Dimensionality reduction via sparse support vector machines. J. Machine Learning Research 3, 1229–1243 (2003) 13. : FS SFS: A novel feature selection method for support vector machines. Pattern Recognition 39(7), 1333–1345 (2006) 14. : Fast variable selection by block addition and block deletion. J. Intelligent Learning Systems & Applications 2(4), 200–211 (2010) 15. : Incremental feature selection.

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