Difference between revisions of "SVM Guidelines"
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To be trained, SVMs require positive and negative training sets. | To be trained, SVMs require positive and negative training sets. | ||
− | Ideally and since there is sometimes discrepancy between what has been flagged and what | + | Ideally and since there is sometimes discrepancy between what has been flagged and what actually gets curated, the best positive training set is the set of papers for which the data type of interest either has been or will be curated. |
Revision as of 13:44, 20 November 2009
To be trained, SVMs require positive and negative training sets.
Ideally and since there is sometimes discrepancy between what has been flagged and what actually gets curated, the best positive training set is the set of papers for which the data type of interest either has been or will be curated.
How many papers are enough?
The answer to this question depends, in part, on the data type. Previous experience with SVMs for first pass curation has indicated that 400 papers is a good starting point, but this number is a guideline, not a fixed rule. Reasonable results may be achieved with fewer papers if the features of a particular data type are distinct, more papers may be needed if features are not as distinct.
What if we don't have enough papers?
Not all data types have 400 curated papers. Some have much less. In these cases, the SVM approach may not be the best tactic. Other options include development of Textpresso categories and author flags.
What is needed to perform the SVMs?
The full text of research articles is needed to perform SVMs for first pass curation. If the full text is not available from an existing Textpresso implementation, Ruihua will need to have the PMID of relevant papers so she can retrieve the full text.