This is the home page of the competition. You don't need a subtitle here. The competition sub-title will appear above. This is where you introduce the problem. You can upload images using the \"select files\" widget on the left in the competition wizard. Upload an image, refresh the page, copy its URL, then insert within the wizard's editor.
\nIf you are copy-pasting from another application, like Word or your browser, try to make sure the html formatting is clean. You can view a page's html using the button at the top right of the editor's toolbar.
\nTo format pages, stick to the following conventions:
\n<p>Paragraphs should go in p tags</p>\n
<pre>Code should go in pre tags</pre>\n
<h2>Subtitles should go in h2 tags</h2>\n
You can display equations using LaTeX enclosed in escaped brackets. For example, this:
\n\\[ \\epsilon = \\sqrt{\\frac{1}{n} \\sum_{i=1}^n (\\log(p_i + 1) - \\log(a_i+1))^2 } \\]
\nis created by this:
\n\\[\n\\epsilon = \\sqrt{\\frac{1}{n} \\sum_{i=1}^n (\\log(p_i + 1) - \\log(a_i+1))^2 }\n\\]\nWe thank Professor Plum, Ph.D. for providing this dataset.
", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "Evaluation", "content": "The evaluation pages describes how submissions will be scored and how students should format their submissions. You don't need a sub-title at the top (the page title appears above). For an example of a high quality evaluation page, see the 2013 KDD Cup page, which we have copied here:
\nThe evaluation metric for this competition is Mean F1-Score. The F1 score, commonly used in information retrieval, measures accuracy using the statistics precision p and recall r. Precision is the ratio of true positives (tp) to all predicted positives (tp + fp). Recall is the ratio of true positives to all actual positives (tp + fn). The F1 score is given by:
\n\\[ F1 = 2\\frac{p \\cdot r}{p+r}\\ \\ \\mathrm{where}\\ \\ p = \\frac{tp}{tp+fp},\\ \\ r = \\frac{tp}{tp+fn} \\]
\nThe F1 metric weights recall and precision equally, and a good retrieval algorithm will maximize both precision and recall simultaneously. Thus, moderately good performance on both will be favored over extremely good performance on one and poor performance on the other.
\nFor every author in the dataset, submission files should contain two columns: AuthorId and DuplicateAuthorIds. DuplicateAuthorIds should be a space-delimited list. Every AuthorId counts as his/her own duplicate, and every duplicate should be listed under each of its respective ids. For example, if you suspect author A, B, and C are the same, you should list (A,A B C), (B,B A C), (C,C A B).
\nThe file should contain a header and have the following format:
\nAuthorId,DuplicateAuthorIds", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false } ] }
1,1