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