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

ISBN-10: 3319444115

ISBN-13: 9783319444116

ISBN-10: 3319444123

ISBN-13: 9783319444123

This booklet constitutes the refereed lawsuits of the 1st ECML PKDD Workshop, AALTD 2015, held in Porto, Portugal, in September 2016.
The eleven complete papers provided have been rigorously reviewed and chosen from 22 submissions. the 1st half specializes in studying new representations and embeddings for time sequence type, clustering or for dimensionality relief. the second one half provides methods on category and clustering with tough functions on drugs or earth statement info. those works convey alternative ways to contemplate temporal dependency in clustering or type tactics. The final a part of the e-book 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 class and clustering.

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Read Online or Download Advanced Analysis and Learning on Temporal Data: First ECML PKDD Workshop, AALTD 2015, Porto, Portugal, September 11, 2015, Revised Selected Papers PDF

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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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2. Pc (τ ) is the fraction of problems on which the performance of classifier c deviates at most by factor τ from the best performance. 3. τmax with Pc (τmax ) = 1 is the maximum factor by which classifier c deviates from the best performance. Dimension Reduction in Dissimilarity Spaces for Time Series Classification 45 References 1. : A complexity-invariant distance measure for time series. In: SIAM International Conference on Data Mining, vol. 11, pp. 699–710 (2011) 2. : A training algorithm for optimal margin classifiers.

The UCR Time Series Classification/Clustering Homepage (2011). ucr. edu/∼eamonn/time series data/ 23. : Feature discovery in non-metric pairwise data. J. Mach. Learn. Res. 5, 801–818 (2004) 24. : Experiencing SAX: a novel symbolic representation of time series. Data Min. Knowl. Discov. 15(2), 107–144 (2007) 25. : Time series classification with ensembles of elastic distance measures. Data Min. Knowl. Discov. 29(3), 565–592 (2015) 26. : Optimized dissimilarity space embedding for labeled graphs.

Structural, Syntactic, and Statistical Pattern Recognition. LNCS, vol. 4109, pp. 287–296. Springer, Heidelberg (2006) 34. : EEG signal classification using PCA, ICA, LDA and support vector machines. Expert Syst. Appl. 37(12), 8659–8666 (2010) 35. : Fast time series classification using numerosity reduction. In: International Conference on Machine Learning (2006) 36. : A brief survey on sequence classification. ACM SIGKDD Explor. Newslett. 12(1), 40–48 (2010) Time Series Classification and Clustering Fuzzy Clustering of Series Using Quantile Autocovariances Borja Lafuente-Rego(B) and Jose A.

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Advanced Analysis and Learning on Temporal Data: First ECML PKDD Workshop, AALTD 2015, Porto, Portugal, September 11, 2015, Revised Selected Papers by Ahlame Douzal-Chouakria, José A. Vilar, Pierre-François Marteau

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