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The pattern-classification algorithm utilizes the training set to train the classifier with the features and the prespecified classes of objects.
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For finding D the K-cluster Single Value Decomposition (K-SVD) method (Aharon et al. 2006) is employed (for details see Section S1 in the Additional file 1), where the matrix (A^{N}) is utilized as the training set for the process.
The GSE21537 dataset used a different, non commercial, microarray platform in contrast to the Affymetrix platform utilized for the training set, which required the predefined phenotype gene sets to be mapped onto the GSE21537 microarray expression dataset.
By utilizing the results of the training set cohort, an additional cohort was conducted and examined using a previously collected database for ovarian cancer in the participating institutions (Gynecologic Oncology Group in Osaka, Japan).
A novelty of the approach is the utilization of the efficient small-sample simulation method Latin Hypercube Sampling (LHS) used for the stochastic preparation of the training set utilized in training the artificial neural network.
This second step utilizes the medication-prescription pairs in the training set for approximate term matching, which enables the algorithm to learn from previously collected information in practice.
For the initial training phase, samples of the training set are utilized to build a classifier.
The synchronized version of the training set is utilized to learn an action-specific model of human motion.
The classifier architecture is illustrated in Figure 4. We utilized 18 basic classifiers to create the training set.
We utilized the six CHO cell lines as the training set and performed tests on a randomized set of 48 unknown samples prepared from these cells that were blinded to the researcher running the test and the analyses.
For illustrative purpose, we first followed the analysis of Shedden et al. [ 21], who utilized the UM and HLM data as the training set and the MSK and DFCI data as the test set.
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