Advanced Analysis and Learning on Temporal Data

Ahlame Douzal-Chouakria

ISBN 10: 3319444115 / ISBN 13: 9783319444116

Published by Springer International Publishing Aug 2016, 2016

About this Item

This item is printed on demand - it takes 3-4 days longer - Neuware -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. 184 pp. Englisch.

Seller Inventory # 9783319444116

Bibliographic Details

• Title: Advanced Analysis and Learning on Temporal Data
• Publisher: Springer International Publishing Aug 2016
• Publication Date: 2016
• Language: English
• ISBN 10: 3319444115
• ISBN 13: 9783319444116
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 235W x 155H x 11D millimeters 235 millimeters width by 155 millimeters height by 11 millimeters depth
• Weight: 289 grams

About this title

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.