Selected article for: "feature space and machine learning"

Author: Laghaout, Amine
Title: Supervised learning on heterogeneous, attributed entities interacting over time
  • Cord-id: 5wonq1dd
  • Document date: 2020_7_22
  • ID: 5wonq1dd
    Snippet: Most physical or social phenomena can be represented by ontologies where the constituent entities are interacting in various ways with each other and with their environment. Furthermore, those entities are likely heterogeneous and attributed with features that evolve dynamically in time as a response to their successive interactions. In order to apply machine learning on such entities, e.g., for classification purposes, one therefore needs to integrate the interactions into the feature engineeri
    Document: Most physical or social phenomena can be represented by ontologies where the constituent entities are interacting in various ways with each other and with their environment. Furthermore, those entities are likely heterogeneous and attributed with features that evolve dynamically in time as a response to their successive interactions. In order to apply machine learning on such entities, e.g., for classification purposes, one therefore needs to integrate the interactions into the feature engineering in a systematic way. This proposal shows how, to this end, the current state of graph machine learning remains inadequate and needs to be be augmented with a comprehensive feature engineering paradigm in space and time.

    Search related documents:
    Co phrase search for related documents
    • loss function and low dimensional: 1
    • loss function and machine learning: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23
    • loss function and machine learning application: 1
    • low dimensional and machine learning: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12
    • low dimensional space and machine learning: 1