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

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

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

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 offered have been conscientiously reviewed and chosen from 22 submissions. the 1st half makes a speciality of studying new representations and embeddings for time sequence type, clustering or for dimensionality aid. the second one half offers techniques on category and clustering with difficult functions on drugs or earth statement info. those works express other ways to think about temporal dependency in clustering or class procedures. The final a part of the publication 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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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 provided have been rigorously reviewed and chosen from 22 submissions. the 1st half specializes in studying new representations and embeddings for time sequence category, clustering or for dimensionality aid.

Additional 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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Figure 3 shows the performance profiles of NN-DTW, NN-EUC, and the best SVM classifier PCA+RBF using classification accuracy as performance metric. It is sufficient to keep the following facts in mind to have a good interpretation of performance profiles: – Each curve represents a classifier and the higher the curve, the better its performance. – Pc (0) is the fraction of problems on which classifier c performed best. – Pc (τ ) is the fraction of problems on which the performance of classifier c deviates at most by factor τ ∈ [0, 1] from the best performance.

B. Jain was funded by the DFG Sachbeihilfe JA 2109/4-1. A Performance Profiles Performance profiles have been introduced by Dolan to compare the efficiency of algorithms [7]. Here, we use performance profiles to compare differences in the classification accuracy of a collection of classifiers on a set of classification problems. The comparison is summarized by one curve per classifier, which is easier to read than a table of classification accuracies. To define a performance profile, we assume that C is a set of classifiers to be compared and P is the set of all classification problems.

Intell. 23(6), 1053–1081 (2009) 33. : Transforming strings to vector spaces using prototype selection. , de Ridder, D. ) 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.

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