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Titolo: | Advanced Analytics and Learning on Temporal Data : 4th ECML PKDD Workshop, AALTD 2019, Würzburg, Germany, September 20, 2019, Revised Selected Papers / / edited by Vincent Lemaire, Simon Malinowski, Anthony Bagnall, Alexis Bondu, Thomas Guyet, Romain Tavenard |
Pubblicazione: | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 |
Edizione: | 1st ed. 2020. |
Descrizione fisica: | 1 online resource (X, 229 p. 109 illus., 90 illus. in color.) |
Disciplina: | 006.31 |
Soggetto topico: | Artificial intelligence |
Computers | |
Computer organization | |
Application software | |
Optical data processing | |
Artificial Intelligence | |
Information Systems and Communication Service | |
Computer Systems Organization and Communication Networks | |
Computer Applications | |
Computer Imaging, Vision, Pattern Recognition and Graphics | |
Persona (resp. second.): | LemaireVincent |
MalinowskiSimon | |
BagnallAnthony | |
BonduAlexis | |
GuyetThomas | |
TavenardRomain | |
Nota di contenuto: | Robust Functional Regression for Outlier Detection -- Transform Learning Based Function Approximation for Regression and Forecasting -- Proactive Fiber Break Detection based on Quaternion Time Series and Automatic Variable Selection from Relational Data -- A fully automated periodicity detection in time series -- Conditional Forecasting of Water Level Time Series with RNNs -- Challenges and Limitations in Clustering Blood Donor Hemoglobin Trajectories -- Localized Random Shapelets -- Feature-Based Gait Pattern Classification for a Robotic Walking Frame -- How to detect novelty in textual data streams? A comparative study of existing methods -- Seq2VAR: multivariate time series representation with relational neural networks and linear autoregressive model -- Modelling Patient Sequences for Rare Disease Detection with Semi-supervised Generative Adversarial Nets -- Extended Kalman Filter for Large Scale Vessels Trajectory Tracking in Distributed Stream Processing Systems -- Unsupervised Anomaly Detection in Multivariate Spatio-Temporal Datasets using Deep Learning -- Learning Stochastic Dynamical Systems via Bridge Sampling -- Quantifying Quality of Actions Using Wearable Sensor -- An Initial Study on Adapting DTW at Individual Query for Electrocardiogram Analysis. |
Sommario/riassunto: | This book constitutes the refereed proceedings of the 4th ECML PKDD Workshop on Advanced Analytics and Learning on Temporal Data, AALTD 2019, held in Würzburg, Germany, in September 2019. The 7 full papers presented together with 9 poster papers were carefully reviewed and selected from 31 submissions. The papers cover 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, on temporal data. . |
Titolo autorizzato: | Advanced Analytics and Learning on Temporal Data |
ISBN: | 3-030-39098-5 |
Formato: | Materiale a stampa |
Livello bibliografico | Monografia |
Lingua di pubblicazione: | Inglese |
Record Nr.: | 9910373928603321 |
Lo trovi qui: | Univ. Federico II |
Opac: | Controlla la disponibilità qui |