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A feasible automated approach would be based on learning the statistical models of each dementia from a set of training samples, where each training sample is associated with a class label.
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To this end, a greedy algorithm is used to find a set of optimal samples from a training set scattered in the parameter space.
(w, c(w)) is a training sample, and D is the set of all training samples.
Using a supervised learning algorithm based on Random Forest, we generated a molecular signature of inflammation from a training set of 77 samples.
28 samples from January 2003 to August 2009 are set as training samples and the others as the testing samples.
In every repetition the training samples were randomly selected from the complete set of training instances.
Data were sampled from the GOSet, and combined with the KnowSet to generate a noisy set of training data.
The training set, T, consists of all training samples of K classes.
We first generate our training set from a simulated population, and each sample is a set of fragments simulated based on a set of selected transcripts from the annotation database.
The tool involves linear discriminant analysis that was first validated in the laboratory with a training set of samples collected from wreckages.
Afterwards, 80 sets of training and testing sample points were then randomly selected from these experimental data and used for the SVM analysis.
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