Cantitate/Preț
Produs

Advanced Analytics and Learning on Temporal Data: Lecture Notes in Computer Science, cartea 12588

Editat de Vincent Lemaire, Simon Malinowski, Anthony Bagnall, Thomas Guyet, Romain Tavenard, Georgiana Ifrim
en Limba Engleză Paperback – 16 dec 2020
This book constitutes the refereed proceedings of the 4th ECML PKDD Workshop on Advanced Analytics and Learning on Temporal Data, AALTD 2019, held in Ghent, Belgium, in September 2020.
The 15 full papers presented in this book were carefully reviewed and selected from 29 submissions. The selected papers are devoted to topics such as Temporal Data Clustering; Classification of Univariate and Multivariate Time Series; Early Classification of Temporal Data; Deep Learning and Learning Representations for Temporal Data; Modeling Temporal Dependencies; Advanced Forecasting and Prediction Models; Space-Temporal Statistical Analysis; Functional Data Analysis Methods; Temporal Data Streams; Interpretable Time-Series Analysis Methods; Dimensionality Reduction, Sparsity, Algorithmic Complexity and Big Data Challenge; and Bio-Informatics, Medical, Energy Consumption, Temporal Data.
Citește tot Restrânge

Din seria Lecture Notes in Computer Science

Preț: 31928 lei

Preț vechi: 39910 lei
-20%

Puncte Express: 479

Carte tipărită la comandă

Livrare economică 17 iunie-01 iulie


Specificații

ISBN-13: 9783030657413
ISBN-10: 3030657418
Pagini: 244
Ilustrații: X, 233 p. 88 illus., 67 illus. in color.
Dimensiuni: 155 x 235 x 14 mm
Greutate: 0.38 kg
Ediția:1st edition 2020
Editura: Springer
Colecția Lecture Notes in Computer Science
Seria Lecture Notes in Computer Science

Locul publicării:Cham, Switzerland

Cuprins

Temporal Data Clustering.- Classification of Univariate and Multivariate Time Series.- Early Classification of Temporal Data.- Deep Learning and Learning Representations for Temporal Data.- Modeling Temporal Dependencies.- Advanced Forecasting and Prediction Models.- Space-Temporal Statistical Analysis.- Functional Data Analysis Methods.- Temporal Data Streams.- Interpretable Time-Series Analysis Methods.- Dimensionality Reduction, Sparsity, Algorithmic Complexity and Big Data Challenge.- Bio-Informatics, Medical, Energy Consumption, Temporal Data.