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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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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 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
  • Data mhc-nips11-v2 2016-01-07 02:25
    Predicting binding affinity of MHC class I molecules. Subset in Krause, Ong, "Contextual Gaussian Process Bandit Optimization", NIPS 2011
  • Data Ying-Yang-shaped 2015-09-10 12:15
    Ying-Yang shaped dataset
  • Data realm-cnsm2015-vod-traces 2015-08-17 15:18
    Linux kernel statistics from a video-streaming cluster and service metrics from a video client
  • Data chemdner-patents-testset 2015-08-14 12:43
    chemdner patents test set text

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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