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    "description": "**Author**:   \r\n**Source**: Unknown - Date unknown  \r\n**Please cite**:   \r\n\r\n**Note: derived from MNIST?**\r\n\r\nDatasets from the Agnostic Learning vs. Prior Knowledge Challenge (http:\/\/www.agnostic.inf.ethz.ch)\r\n\r\nDataset from: http:\/\/www.agnostic.inf.ethz.ch\/datasets.php\r\n\r\nModified by TunedIT (converted to ARFF format)\r\n\r\n\r\nGINA is digit recognition database\r\n\r\nThe task of GINA is handwritten digit recognition. For the \"prior knowledge track\" we chose the problem of separating one-digit odd numbers from one-digit even numbers. The original pixel map representation is provided. This is a two class classification problem with sparse continuous input variables, in which each class is composed of several clusters. It is a problem with heterogeneous classes.\r\n\r\nData type: non-sparse\r\nNumber of features: 784\r\nNumber of examples and check-sum:\r\nPos_ex Neg_ex Tot_ex Check_sum\r\nTrain  1550  1603  3153 82735983.00\r\nValid   155   160   315 8243382.00\r\n\r\nThis dataset contains samples from both training and validation datasets.",
    "format": "ARFF",
    "uploader": "Joaquin Vanschoren",
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    "visibility": "public",
    "creator": "IJCNN 2007 Workshop on Agnostic Learning vs. Prior Knowledge",
    "contributor": null,
    "date": "2014-10-06 23:56:12",
    "update_comment": null,
    "last_update": "2014-10-06 23:56:12",
    "licence": "Public",
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