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Choose between

  • A raw data set.
  • A learning task defined on existing data sets.
  • Describing a machine learning method.
  • Creating a challenge by grouping existing tasks.

Recent Items

  • Data trn 2017-09-17 07:32
  • Data realm-nem2017-traces 2017-07-12 11:18
    Linux kernel statistics from video-streaming and key-value cluster and service metrics from clients
  • Challenge LCDDIS 2017-06-15 21:54
  • Data ContemptExpressions 2016-08-09 16:04
    a recopilation of faces showing contempt
  • Data mhc-nips11 2016-01-07 02:27
    (see mhc-nips11-v2). Predicting binding affinity of MHC class I molecules. Subset in Krause, Ong, "Contextual Gaussian Process Bandit Optimization", NIPS 2011

How does it work?

This repository manages the following types of objects.
  • Data Sets - Raw data as a collection of similarily structured objects.
  • Material and Methods - Descriptions of the computational pipeline.
  • Learning Tasks - Learning tasks defined on raw data.
  • Challenges - Collections of tasks which have a particular theme.
Between data sets and tasks, the relationship is one-to-many, as a data set can give rise to many different learning tasks. A method can also be applied to several different tasks, giving rise to solutions. On the other hand, a task can have many solutions, but each solution belongs to a certain learning task. These relationships are illustrated in the image.


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