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Traditional methods need to built a classifier for each flower category, and obtain large number of flower samples to train these classifiers.
The researchers also built a classifier for whether someone was in a relationship, based on their call and text records, and once they had labeled a person as being in a relationship identifying their partner was "trivial" — again from the metadata.
Combining the two approaches, we built a classifier for rapid and accurate annotation of core photosystem-II genes and transcripts to seven taxonomic classes of cyanobacteria and cyanophages.
As described above, we built a classifier for each individual gene, which required creating an annotated data set for each gene.
In addition, in order to make use of the information in the annotated sentences to improve the performance, after the ranking, we built a classifier for 12 high-level GO classes trained on labeled sentences to prune the result.
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Logistic regression was used to build a classifier for prognosis.
Finally, a selection of these features is used to build a classifier for each enzymatic transformation, predicting for novel compounds whether that transformation is likely to occur.
We have presented a method of integrating biologically derived gene expression profiles and clinical data to build a classifier for local recurrence after breast-conserving therapy.
Finally, we used the low-dimensional embedding found by Isomap to build a classifier for prediction of apoptosis intensity (question 5).
Since there are around 40 000 GO terms in the GO database and only around 500 terms in the training data, it is difficult to build a classifier for the whole vocabulary of GO terms, but it is much easier to build a classifier for high-level GO terms, since the vocabulary becomes much smaller when moving to the root of the Ontology concept tree.
If one study is used to derive a gene list, and this gene list is used to build a classifier for another study, a decrease in accuracy can be observed.
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