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SSL methods use a few labeled samples, and leverage the information from many unlabeled samples to train an accurate classifier.
One key issue for most classification algorithms is that they need large amounts of labeled samples to train the classifier.
Traditional methods need to built a classifier for each flower category, and obtain large number of flower samples to train these classifiers.
In the process of the international early warning for securities systemic risk, the uniform design method (UDM) and boundary value method(BVM) are used to design the uniform, large-scale and representative samples to train the RBF neural network (RBFNN).
Specifically, the proposed approach uses fraudulent samples and non-fraudulent samples to train a pair of dual GHSOMs under the same training parameters and examines the hypotheses for counterpart relationships among their subgroups taking advantage of unsupervised learning nature and growing hierarchical structures from GHSOMs.
There aren't enough samples to train the net?
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The training active samples (TrN 1) are fixed among all the training data sub-samples, while the inactive samples will be sampled without replacement from TrN 0. There will be NoS sub-samples to train the model on.
A small set of protein interactions provides a small sample to train parameters of prediction methods, thus leading to low confidence.
RF accuracy assessment traditionally employs internal cross-validation between in-bag samples used to train the trees, and out-of-bag samples used for model validation57.
(a) Data samples used to train the decision trees.
The problem, as one researcher explained to Motherboard, was that the image samples used to train the algorithms weren't balanced in terms of race and ethnicity.
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