Uploaded image for project: 'Architecture for Learning Enabled Correlation (ALEC)'
  1. Architecture for Learning Enabled Correlation (ALEC)
  2. ALEC-102

Hellinger Distance for ALEC: Calculate distance.

    XMLWordPrintable

Details

    • Story
    • Status: Closed (View Workflow)
    • Minor
    • Resolution: Done
    • None
    • None
    • None

    Description

      Implement Hellinger Distance calculation using dummy data. This is a POC step to learn how to calculate it. Once this is achieved, we can proceed to train with real data and implement into ALEC for comparison purposes.

      GOALS:

      • Learn the correct design of the neural network that helps fit timestamps to a distribution.
      • Use the parameters learnt by the neural network to calculate Hellinger Distance.

      DONE CRITERIA:

      • Generate dummy data.
      • Write and test Neural network that learns values necessary to calculate Hellinger Distance.
      • Calculate Hellinger Distance using with dummy data.

      This doesn't include:

      • Training with "real" data.
      • ALEC implementation.
      • Comparison with current distance measures for either clustering (DBSCAN) or classification (Deep Learning) engines.

       

       

      Attachments

        Activity

          People

            gmantecon Gerardo Mantecon
            joseanes Jose
            Votes:
            0 Vote for this issue
            Watchers:
            1 Start watching this issue

            Dates

              Created:
              Updated:
              Resolved: