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Briefly, this method is composed of three steps: (i) label the patterns from the test set (an unlabelled data block), using an unsupervised method; (ii) compute some statistics from the train and test sets, conditioned to the given class labels for train set; and (iii) compare the training and testing statistics using a multivariate hypothesis test.
Our goal in using an unsupervised method is to get an approximate grouping of data.
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We use an unsupervised method to define the threshold, γ, above which a gene-level histone score is accepted to represent actual regulatory activity.
To reveal groups of genes that act together we first used an unsupervised method-principal component analysis-to describe the correlation structure of the selected genes.
The existing complex sets predicted by combining Gavin and Krogan data for comparison include: Hart: the predicted complex set from Hart et al. (2007 ), using a supervised method; Pu: the predicted complex set from Pu et al. (2007 ), using a supervised method; and BT-893: the predicted complex set from Friedel et al. (2009 ), using an unsupervised Bootstrap method.
Hart: the predicted complex set from Hart et al. (2007), using a supervised method; Pu: the predicted complex set from Pu et al. (2007), using a supervised method; and BT-893: the predicted complex set from Friedel et al. (2009), using an unsupervised Bootstrap method.
The combined images (NAIP bands + NDVI band) were classified using an unsupervised classification method (Xie et al. 2008; Perotto-Baldivieso et al. 2009).
In Stage 1, a set of clusters is identified using an unsupervised clustering method which is simultaneously applied to the training feature vectors extracted from the different classes.
By using an unsupervised training method, the HMM values can be trained without additional input from the user, such as known genome annotations.
Another commonly used genotype-calling algorithm, Illuminus (Teo et al., 2007), also designed for Illumina arrays, again clusters intensity data across samples on a per SNP basis, using an unsupervised clustering method based on a mixture model of Student's t-distributions.
In a pilot study of acute meningitis, CSF spectra from small numbers of controls, patients with viral meningitis and bacterial/fungal meningitis were distinguished using an unsupervised cluster analysis method [8], suggesting that classification according to etiology is possible using larger data sets.
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