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We built this web site as a repository for your machine learning data.
Upload your data, find interesting data sets, exchange solutions, compare yourself against other methods.

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  • 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 Climate Model Simulation Crashes 2013-06-18 22:13
    Given Latin hypercube samples of 18 climate model input parameter values, predict climate model simulation crashes and determine the parameter value combinations that cause the failures.
  • Data TSS 2013-05-31 00:52
    Transcription Start Site Detection with Multiple Kernels
  • Data seed data set 2013-05-26 20:02
    my data
  • Challenge CHEMDNER 2012-12-31 12:32
    Chemical compound and drug name recognition task (BioCreative IV: http://www.biocreative.org/tasks/biocreative-iv/chemdner )
  • Data BioCreative III PPI ACT dataset 2012-12-31 12:22
    Dataset used for the article classification task of BioCreative III (see http://www.biocreative.org/tasks/biocreative-iii/ppi/)

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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