By Neamat El Gayar, Friedhelm Schwenker, Cheng Suen

This publication constitutes the refereed complaints of the sixth IAPR TC3 overseas Workshop on man made Neural Networks in trend attractiveness, ANNPR 2014, held in Montreal, quality controls, Canada, in October 2014. The 24 revised complete papers provided have been rigorously reviewed and chosen from 37 submissions for inclusion during this quantity. They hide a wide range of subject matters within the box of studying algorithms and architectures and discussing the newest learn, effects, and ideas in those areas.

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Extra 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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Early results for named entity recognition with conditional random fields, feature induction and web-enhanced lexicons. In: Proceedings of CoNLL (2003) 2. : Sentence-based active learning strategies for information extraction. In: Proceedings of the 2nd Italian Information Retrieval Workshop (IIR 2010), pp. 41–45 (2010) 3. : Active learning for information extraction with multiple view. In: Proceedings of the European Conference in Machine Learning (ECML 2003), vol. 77, pp. 257–286 (2003) 4. : Inducing multilingual text analysis tools via robust projection across aligned corpora.

Joint bilingual name tagging for parallel corpora. In: Proceedings of CIKM 2012 (2012) 9. : Using word-dependent transition models in HMM based word alignment for statistical machine translation. In: Proceedings of the Second Workshop on SMT (WMT). Association for Computational Linguistics (2007) 10. : Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In: Proceedings of the International Conference on Machine Learning (ICML), pp. 282–289 (2001) 11. : A tutorial on hidden markov models and selected applications in speech recognition.

Neural Comput. : Two strategies to avoid overfitting in feedforward networks. : A dynamical system perspective of structural learning with forgetting. Trans. Neur. Netw. : Structural learning with forgetting. Neural Netw. : A new algorithm to design compact two-hidden-layer artificial neural networks. Neural Netw. M. : New training strategies for constructive neural networks with application to regression problems. Neural Netw. : Advances in neural information processing systems 2, pp. 598–605. : A penalty-function approach for pruning feedforward neural networks.

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