Advanced Analysis and Learning on Temporal Data [electronic resource] : First ECML PKDD Workshop, AALTD 2015, Porto, Portugal, September 11, 2015, Revised Selected Papers / edited by Ahlame Douzal-Chouakria, Jos�e A. Vilar, Pierre-Fran�cois Marteau.
Contributor(s): Douzal-Chouakria, Ahlame [editor.] | Vilar, Jos�e A [editor.] | Marteau, Pierre-Fran�cois [editor.] | SpringerLink (Online service).
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BookSeries: Lecture Notes in Computer Science: 9785Publisher: Cham : Springer International Publishing : Imprint: Springer, 2016Description: X, 173 p. 64 illus. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783319444123.Subject(s): Computer science | Algorithms | Database management | Information storage and retrieval | Artificial intelligence | Computer Science | Artificial Intelligence (incl. Robotics) | Database Management | Information Systems Applications (incl. Internet) | Information Storage and Retrieval | Algorithm Analysis and Problem ComplexityAdditional physical formats: Printed edition:: No titleDDC classification: 006.3 Online resources: Click here to access online
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Springer eBooksSummary: 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. .
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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