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Internet-Advertisements

Internet-Advertisements

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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

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