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Internet-Advertisements

Internet-Advertisements

active ARFF Publicly available Visibility: public Uploaded 30-10-2014 by Joaquin Vanschoren
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XSD does not comply. XSD errors: XML does not correspond to XSD schema. Error Element '{http://openml.org/openml}error': [facet 'maxLength'] The value has a length of '1140'; this exceeds the allowed maximum length of '1024'. on line 4 column 0. Error Element '{http://openml.org/openml}error': 'Problem validating uploaded description file: XML does not correspond to XSD schema. Error Element '{http://openml.org/openml}name': [facet 'pattern'] The value 'origurl*target+®ion' is not accepted by the pattern '\p{IsBasicLatin}*'. on line 8030 column 0. Error Element '{http://openml.org/openml}name': 'origurl*target+®ion' is not a valid value of the atomic type '{http://openml.org/openml}basic_latin64'. on line 8030 column 0. Error Element '{http://openml.org/openml}name': [facet 'pattern'] The value 'origurl*®ion+0' is not accepted by the pattern '\p{IsBasicLatin}*'. on line 9067 column 0. Error Element '{http://openml.org/openml}name': 'origurl*®ion+0' is not a valid value of the atomic type '
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Author: Nicholas Kushmerick Source: [UCI](http://archive.ics.uci.edu/ml/datasets/Internet+Advertisements) - 1998 Please cite: This dataset represents a set of possible advertisements on Internet pages. The features encode the geometry of the image (if available) as well as phrases occurring in the URL, the image's URL and alt text, the anchor text, and words occurring near the anchor text. The task is to predict whether an image is an advertisement ("ad") or not ("nonad"). Relevant Papers: N. Kushmerick (1999). "Learning to remove Internet advertisements", 3rd Int Conf Autonomous Agents. Available at: http://rexa.info/paper/2fdc1cee89b7f4f2c9227d6f5d9b05d22c5ab3e9

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10 tasks

0 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: class
0 runs - estimation_procedure: Leave one out - target_feature: class
0 runs - estimation_procedure: 33% Holdout set - target_feature: class
0 runs - estimation_procedure: 10% Holdout set - target_feature: class
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - target_feature: class
0 runs - estimation_procedure: 5 times 2-fold Crossvalidation - target_feature: class
0 runs - estimation_procedure: Test on Training Data - target_feature: class
0 runs - estimation_procedure: 10 times 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: class
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