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Although the objective of the DAE approach is to recover the original clean signals, the focus of the noisy training approach proposed here is to construct a robust classifier.
Ultimately, we want to construct a robust classifier that yields accurate and consistent classification results on independent gene expression datasets.
To construct a robust classifier, data redundancy was removed and the dataset had less than 25% identity among the amino acid sequences.
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AdaBoost, introduced by Freund and Schapire [ 35], is an algorithm that constructs a robust classifier as a linear combination of weak classifiers.
In this paper, we shall construct a robust test for the mean change in a sequence.
The second layer of phishGILLNET (phishGILLNET2) employs AdaBoost to build a robust classifier.
The second layer of phishGILLNET (phishGILLNET2) employs classifier ensemble technique AdaBoost and topic probabilities as features to build a robust classifier using several base learners.
This layer employs AdaBoost and Co-Training algorithm to build a robust classifier using large corpus of unlabeled data.
Here we design a new nonparallel support vector machine (U-NSVM) that can exploit prior knowledge embedded in the universum to construct a more robust classifier for training.
For this purpose a robust classifier is necessary.
phishGILLNET3 builds a robust classifier using only a fraction of labeled samples and applying Co-Training to label additional samples.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com