Muutke küpsiste eelistusi

E-raamat: Advanced Analytics and Learning on Temporal Data: 8th ECML PKDD Workshop, AALTD 2023, Turin, Italy, September 18-22, 2023, Revised Selected Papers

Edited by , Edited by , Edited by , Edited by , Edited by , Edited by , Edited by
Teised raamatud teemal:
  • Formaat - EPUB+DRM
  • Hind: 74,09 €*
  • * hind on lõplik, st. muud allahindlused enam ei rakendu
  • Lisa ostukorvi
  • Lisa soovinimekirja
  • See e-raamat on mõeldud ainult isiklikuks kasutamiseks. E-raamatuid ei saa tagastada.
Advanced Analytics and Learning on Temporal Data: 8th ECML PKDD Workshop, AALTD 2023, Turin, Italy, September 18-22, 2023, Revised Selected PapersSuurem pilt
Teised raamatud teemal:

DRM piirangud

  • Kopeerimine (copy/paste):

    ei ole lubatud

  • Printimine:

    ei ole lubatud

  • Kasutamine:

    Digitaalõiguste kaitse (DRM)
    Kirjastus on väljastanud selle e-raamatu krüpteeritud kujul, mis tähendab, et selle lugemiseks peate installeerima spetsiaalse tarkvara. Samuti peate looma endale  Adobe ID Rohkem infot siin. E-raamatut saab lugeda 1 kasutaja ning alla laadida kuni 6'de seadmesse (kõik autoriseeritud sama Adobe ID-ga).

    Vajalik tarkvara
    Mobiilsetes seadmetes (telefon või tahvelarvuti) lugemiseks peate installeerima selle tasuta rakenduse: PocketBook Reader (iOS / Android)

    PC või Mac seadmes lugemiseks peate installima Adobe Digital Editionsi (Seeon tasuta rakendus spetsiaalselt e-raamatute lugemiseks. Seda ei tohi segamini ajada Adober Reader'iga, mis tõenäoliselt on juba teie arvutisse installeeritud )

    Seda e-raamatut ei saa lugeda Amazon Kindle's. 

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.
Lisainfo e-raamatute kohta Lisainfo e-raamatute kohta
Püsilink: https://www.kriso.ee/db/97830314989616e.html