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Herausgeber: 
  • Andreas Hotho
  • Elisa Fromont
  • Arno Knobbe
  • Marloes Maathuis
  • Ulf Brefeld
  • Céline Robardet
  • Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2019, Würzburg, Germany, September 16-20, 2019, Proceedings, Par 
     

    (Buch)
    Dieser Artikel gilt, aufgrund seiner Grösse, beim Versand als 3 Artikel!


    Übersicht

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    Lieferstatus:   i.d.R. innert 5-10 Tagen versandfertig
    Veröffentlichung:  Mai 2020  
    Genre:  EDV / Informatik 
     
    angewandte informatik / ArtificialIntelligence / bayesiannetworks / Classification / classificationmethods / Computerhardware / ComputerNetworks / computersystems
    ISBN:  9783030461461 
    EAN-Code: 
    9783030461461 
    Verlag:  Springer 
    Einband:  Kartoniert  
    Sprache:  English  
    Dimensionen:  H 235 mm / B 155 mm / D 41 mm 
    Gewicht:  1130 gr 
    Seiten:  760 
    Bewertung: Titel bewerten / Meinung schreiben
    Inhalt:
    The three volume proceedings LNAI 11906 ¿ 11908 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2019, held in Würzburg, Germany, in September 2019. The total of 130 regular papers presented in these volumes was carefully reviewed and selected from 733 submissions; there are 10 papers in the demo track. The contributions were organized in topical sections named as follows: Part I: pattern mining; clustering, anomaly and outlier detection, and autoencoders; dimensionality reduction and feature selection; social networks and graphs; decision trees, interpretability, and causality; strings and streams; privacy and security; optimization. Part II: supervised learning; multi-label learning; large-scale learning; deep learning; probabilistic models; natural language processing. Part III: reinforcement learning and bandits; ranking; applied data science: computer vision and explanation; applied data science: healthcare; applied data science: e-commerce, finance, and advertising; applied data science: rich data; applied data science: applications; demo track. Chapter "Incorporating Dependencies in Spectral Kernels for Gaussian Processes" is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.

      



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