Advanced Analysis and Learning on Temporal Data: First ECML by Ahlame Douzal-Chouakria, José A. Vilar, Pierre-François

By Ahlame Douzal-Chouakria, José A. Vilar, Pierre-François Marteau

This publication constitutes the refereed court cases of the 1st ECML PKDD Workshop, AALTD 2015, held in Porto, Portugal, in September 2016.
The eleven complete papers awarded have been conscientiously reviewed and chosen from 22 submissions. the 1st half specializes in studying new representations and embeddings for time sequence type, clustering or for dimensionality aid. the second one half offers ways on type and clustering with not easy functions on drugs or earth commentary facts. those works convey alternative ways to contemplate temporal dependency in clustering or type methods. The final a part of the booklet is devoted to metric studying and time sequence comparability, it addresses the matter of speeding-up the dynamic time warping or facing multi-modal and multi-scale metric studying for time sequence type and clustering.

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Extra info for Advanced Analysis and Learning on Temporal Data: First ECML PKDD Workshop, AALTD 2015, Porto, Portugal, September 11, 2015, Revised Selected Papers

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6 % of all cases. Dissimilarity spaces endowed with the Euclidean distance form a distorted representation of the time series space endowed with the DTW distance in such a way that neighborhood relations are not properly preserved. In most cases, these distortions impact classification results negatively, often by a large margin (see Table 1). In the few cases where the distortions improve classification results, the improvements are only small and could also be occurred by chance due to the random sampling of the training and test set.

24(1), 164–181 (2011) 11. : SVM learning with the SH inner product. In: European Symposium on Artificial Neural Networks (2004) 12. : Pattern extraction for time series classification. , De Raedt, L. ) PKDD 2001. LNCS (LNAI), vol. 2168, pp. 115–127. Springer, Heidelberg (2001) 13. : Classification on pairwise proximity data. In: Advances in Neural Information Processing Systems (1999) 14. : Classification on proximity data with LPmachines. In: International Conference on Artificial Neural Networks (1999) 15.

Time series classification in dissimilarity spaces. In: Proceedings of the 1st International Workshop on Advanced Analytics and Learning on Temporal Data (2015) 46 B. Jain and S. Spiegel 20. : Generalized gradient learning on time series. Mach. Learn. 100(2), 587– 608 (2015) 21. : Using dynamic time warping distances as features for improved time series classification. Data Min. Knowl. Discov. 30(2), 283–312 (2016) 22. : The UCR Time Series Classification/Clustering Homepage (2011). ucr. edu/∼eamonn/time series data/ 23.

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