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Upload your data, find interesting data sets, exchange solutions, compare yourself against other methods.

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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 mkl-splice 2015-05-28 16:20
    Splice Site Detection Using Multiple Kernels (20 WD Kernels)
  • Data Mauna Loa atmospheric CO2 2015-04-02 17:00
    Atmospheric CO2 from Continuous Air Samples at Mauna Loa Observatory, Hawaii, U.S.A. Period of Record: March 1958 - December 2001
  • Data realm-im2015-vod-traces 2014-12-10 17:44
    Linux kernel statistics from a video server and service metrics from a video client
  • Data mhc-affinity 2014-12-06 07:33
    Binding affinity of MHC class I molecules
  • Data mhc-nips11 2014-12-06 07:20
    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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