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Advanced Analytics and Learning on Temporal Data: 8th ECML PKDD Workshop, AALTD 2023, Turin, Italy, September 1822, 2023, Revised Selected Papers 1st ed. 2023 [Pehme köide]

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  • Formaat: Paperback / softback, 308 pages, kõrgus x laius: 235x155 mm, kaal: 498 g, 90 Illustrations, color; 23 Illustrations, black and white; XIII, 308 p. 113 illus., 90 illus. in color., 1 Paperback / softback
  • Sari: Lecture Notes in Artificial Intelligence 14343
  • Ilmumisaeg: 20-Dec-2023
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 303149895X
  • ISBN-13: 9783031498954
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Advanced Analytics and Learning on Temporal Data: 8th ECML PKDD Workshop, AALTD 2023, Turin, Italy, September 1822, 2023, Revised Selected Papers 1st ed. 2023Suurem pilt
  • Formaat: Paperback / softback, 308 pages, kõrgus x laius: 235x155 mm, kaal: 498 g, 90 Illustrations, color; 23 Illustrations, black and white; XIII, 308 p. 113 illus., 90 illus. in color., 1 Paperback / softback
  • Sari: Lecture Notes in Artificial Intelligence 14343
  • Ilmumisaeg: 20-Dec-2023
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 303149895X
  • ISBN-13: 9783031498954
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9783031498954.html
This volume LNCS 14343 constitutes the refereed proceedings of the 8th ECML PKDD Workshop, AALTD 2023, in Turin, Italy, in September 2023.  





The 20 full papers were carefully reviewed and selected from 28 submissions. They are organized in the following topical section as follows: Machine Learning; Data Mining; Pattern Analysis; Statistics to Share their Challenges and Advances in Temporal Data Analysis.





 
Human Activity Segmentation Challenge.- Human Activity Segmentation
Challenge@ECML/PKDD23.- Change points detection in multivariate signal
applied to human activity segmentation.- Change Point Detection via Synthetic
Signals.- Oral Presentation.- Clustering time series with k-medoids based
algorithms.- Explainable Parallel RCNN with Novel Feature Representation
for Time Series Forecasting.- RED CoMETS: an ensemble classifier for
symbolically represented multivariate time series.- Deep Long Term Prediction
for Semantic Segmentation in Autonomous Driving.- Extracting Features from
Random Subseries: A Hybrid Pipeline for Time Series Classification and
Extrinsic Regression.- ShapeDBA: Generating Effective Time Series Prototypes
using ShapeDTW Barycenter Averaging.- Poster Presentation.- Temporal
Performance Prediction for Deep Convolutional Long Short-Term Memory
Networks.- Evaluating Explanation Methods for Multivariate Time
SeriesClassification.- tGLAD: A sparse graph recovery based approach for
multivariate time series segmentation.- Designing a New Search Space for
Multivariate Time-Series Neural Architecture Search.- Back to Basics: A
Sanity Check on Modern Time Series Classification Algorithms.- Do Cows Have
Fingerprints? Using Time Series Techniques and Milk Flow Profiles to
Characterise Cow Behaviours and Detect Health Issues.- Exploiting Context and
Attention with Recurrent Neural Network for Sensor Time Series
Prediction.- Rail Crack Propagation Forecasting Using Multi-horizons
RNNs.- Electricity Load and Peak Forecasting: Feature
Engineering, Probabilistic LightGBM and Temporal Hierarchies.- Time-aware
Predictions of Moments of Change in Longitudinal User Posts on Social Media.