Read e-book online Engineering Applications of Neural Networks: 17th PDF

By Chrisina Jayne, Lazaros Iliadis

This booklet constitutes the refereed complaints of the seventeenth overseas convention on Engineering functions of Neural Networks, EANN 2016, held in Aberdeen, united kingdom, in September 2016.

The 22 revised complete papers and 3 brief papers offered including tutorials have been conscientiously reviewed and chosen from forty-one submissions. The papers are equipped in topical sections on lively studying and dynamic environments; semi-supervised modeling; type purposes; clustering purposes; cyber-physical structures and cloud purposes; time-series prediction; learning-algorithms.

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Read Online or Download Engineering Applications of Neural Networks: 17th International Conference, EANN 2016, Aberdeen, UK, September 2-5, 2016, Proceedings PDF

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83(6), 1115–1128 (2013) 3. : Incremental learning of concept drift from streaming imbalanced data. IEEE Trans. Knowl. Data Eng. 25(10), 2283–2301 (2013) 4. : The problem of concept drift: definitions and related work, Department of Computer Science, Trinity College Dublin. Technical report (2004) 5. : DOTS: drift oriented tool system. , Liu, Q. ) ICONIP 2015. LNCS, vol. 9492, pp. 615–623. Springer, Heidelberg (2015) 6. : Effective learning in dynamic environments by explicit context tracking. In: Proceedings of European Conference on Machine Learning, pp.

9492, pp. 615–623. Springer, Heidelberg (2015) 6. : Effective learning in dynamic environments by explicit context tracking. In: Proceedings of European Conference on Machine Learning, pp. 227–243 (1993) 7. : Defining semantic meta-hashtags for twitter classification. , Buesser, P. ) ICANNGA 2013. LNCS, vol. 7824, pp. 226–235. Springer, Heidelberg (2013) 8. : Immune system approaches to intrusion detection - a review. Nat. Comput. 6(4), 413–466 (2007) 9. : Incremental learning of concept drift in nonstationary environments.

We have used a classification strategy previously introduced in [7], where the Twitter message hashtag is used to label the content of the message, which means that yi represents the hashtag that labels the Twitter message xi . Notwithstanding it is a multi-class problem in its essence, it can be decomposed in multiple binary tasks in a one-against-all binary classification strategy. In this case, a classifier ht is composed by |Y | binary classifiers. 2 Learning Models We are focusing on dynamic ensembles in text classification scenarios, where the ensemble must adapt to deal with changes usually dependent on hidden contexts.

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