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

Ahlame Douzal-Chouakria

ISBN 10: 3319444115 / ISBN 13: 9783319444116

Edité par Springer International Publishing, 2016

A propos de cet article

Druck auf Anfrage Neuware - Printed after ordering - 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.

N° de réf. du vendeur 9783319444116

Détails bibliographiques

• Titre: Advanced Analysis and Learning on Temporal Data : First ECML PKDD Workshop, AALTD 2015, Porto, Portugal, September 11, 2015, Revised Selected Papers
• Éditeur: Springer International Publishing
• Date de parution: 2016
• Langue: anglais
• ISBN à 10 chiffres: 3319444115
• ISBN à 13 chiffres: 9783319444116
• Reliure: Taschenbuch
• État de l'article: Neu
• Dimensions: 235 L x 155 H x 11 P (en millimètres) 235 millimètres de largeur par 155 millimètres de hauteur par 11 millimètres de profondeur
• Poids: 289 grammes

A propos de ce titre

Synopsis

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.