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

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Ahlame Douzal-Chouakria, José A. Vilar, Pierre-François Marteau
Springer, 3 août 2016 - 173 pages
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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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Pages sélectionnées

Table des matières

A Hierarchical Coclustering Formalization
3
Dense BagofTemporalSIFTWords for Time Series Classification
17
Dimension Reduction in Dissimilarity Spaces for Time Series Classification
31
Time Series Classification and Clustering
47
Fuzzy Clustering of Series Using Quantile Autocovariances
49
A Reservoir Computing Approach for Balance Assessment
65
Learning Structures in Earth Observation Data with Gaussian Processes
78
Monitoring Short Term Changes of Infectious Diseases in Uganda with Gaussian Processes
95
Recent Methods and Results
111
Metric Learning for Time Series Comparison
129
A Multimodal Metric Learning Framework for Time Series kNN Classification
130
A Comparison of Progressive and Iterative Centroid Estimation Approaches Under Time Warp
144
CoarseDTW for Sparse Time Series Alignment
157
Author Index
173
Droits d'auteur

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Ahlame Douzal-Chouakria,José A. Vilar,Pierre-François Marteau
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Expressions et termes fréquents

algorithm alignment analysis approach average Bayesian centroid changepoints classification accuracy Coarse-DTW coclustering combination component considered corresponding covariance D-BoTSW Data Mining data points datasets defined dissimilarity representations dissimilarity space distribution Douzal-Chouakria dQAF DTW distance dynamic time warping error rate estimation Euclidean evaluation example framework function fuzzy clustering GARCH Gaussian process regression Gaussian processes global GP models graph graphical lasso graphical models health facilities heteroscedastic IEEE input International Conference interval iterative kernel keypoints L2-norm length LI-ESN linear machine learning medoid methods metric learning MODL Neural nlaaf NN-DTW number of clusters optimal output pairwise parameter PCA+RBF performance profiles precision matrix problem proposed prototypes quantile quantile autocovariances regression retrieval samples scale selection sequence series classification signal sparse time series Springer statistical structure support vector machine SVM classifiers temporal data values variables wdtw WGPR Wii Balance Board

Informations bibliographiques

QR code for Advanced Analysis and Learning on Temporal Data
TitreAdvanced Analysis and Learning on Temporal Data: First ECML PKDD Workshop, AALTD 2015, Porto, Portugal, September 11, 2015, Revised Selected Papers
Volume 9785 de Lecture Notes in Computer Science
Lecture Notes in Artificial Intelligence
RédacteursAhlame Douzal-Chouakria, José A. Vilar, Pierre-François Marteau
ÉditeurSpringer, 2016
ISBN3319444123, 9783319444123
Longueur173 pages
  
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