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In our supervised learning experiments we compare the accuracy for two popular supervised learning algorithms: a Naive Bayes (NB) classifier and a support vector machine (SVM) classifier [28].
In our supervised recognition case, just like in many other computer vision tasks, features are histogram-based, and are not naturally represented in the space.
We also describe the classification functions used in our supervised learning experiments, such as naive Bayes, logistic regression, and support vector machines.
Next we address the case of characteristic kernels which are defined for Abelian semigroups in Section 4. It gives us a characteristic kernel which is proper for histogram-based feature descriptors that we use in our supervised method.
The reason for using five sub-users is that we in this way construct several training instances for each user in order to facilitate the learning phase in our supervised learning experiments.
Some of the genes that comprised the general stress signature for each cell type were also found to have toxicant-specific expression in our supervised analyses.
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In our approach, supervised learning is performed taking advantage of relevance labels provided by users.
In our previous supervised unmixing work, objects were simply defined as contiguous pixel regions above a global threshold.
Additional file 2: Table S3 gives the estimated regression coefficient and the posterior probability for each external data type in our revised supervised framework.
In our semi-supervised method, we use all information available on both unlabeled and labeled data.
In our semi-supervised method, we use the unsupervised Pairwise Recommendation [9] re-ranking algorithm, which has been demonstrated to yield effective results in image retrieval tasks.
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