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

Ahlame Douzal-Chouakria (u. a.)

ISBN 10: 3319444115 ISBN 13: 9783319444116

Editorial: Springer International Publishing, 2016

Idioma: Ingl�s

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Detalles bibliogr�ficos

T�tulo

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

Editor

Springer International Publishing

Fecha de publicaci�n

2016

Idioma

Ingl�s

ISBN 10

3319444115

ISBN 13

9783319444116

Encuadernaci�n

Taschenbuch

Estado

Neu

Dimensiones

Peso

289 gramos

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Sinopsis

This book constitutes the refereed proceedings of the First ECML PKDD Workshop, AALTD 2015, held in Porto, Portugal, in September 2016.
The 11 full papers presented were carefully reviewed and selected from 22 submissions. The first part focuses on learning new representations and embeddings for time series classification, clustering or for dimensionality reduction. The second part presents approaches on classification and clustering with challenging applications on medicine or earth observation data. These works show different ways to consider temporal dependency in clustering or classification processes. The last part of the book is dedicated to metric learning and time series comparison, it addresses the problem of speeding-up the dynamic time warping or dealing with multi-modal and multi-scale metric learning for time series classification and clustering.

Rese�a del editor

This book constitutes the refereed proceedings of the First ECML PKDD Workshop, AALTD 2015, held in Porto, Portugal, in September 2016. The 11 full papers presented were carefully reviewed and selected from 22 submissions. The first part focuses on learning new representations and embeddings for time series classification, clustering or for dimensionality reduction. The second part presents approaches on classification and clustering with challenging applications on medicine or earth observation data. These works show different ways to consider temporal dependency in clustering or classification processes. The last part of the book is dedicated to metric learning and time series comparison, it addresses the problem of speeding-up the dynamic time warping or dealing with multi-modal and multi-scale metric learning for time series classification and clustering.

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