Sentence examples for supervised method that from inspiring English sources

However adaptations have been described that allow SOMs to be used in a supervised method, that is for predictive modelling [55, 56, 61 63].

In contrast to PCA, which is an unsupervised multivariate statistical classification method that works to explain maximum variation between samples, PLS-DA is a supervised method that explains maximum separation between pre-defined classes (e.g. genotype or health state) in the data.

O-PLS-DA, a supervised method that uses prior information, was used to better discriminate between clusters.

For α close to 0, diffusion of labels plays a small role in determining the ranking and LP behaves like a supervised method that computes a correlation measure.

Therefore, we propose a supervised method that applies pathway information to summarize similarity statistics to a consensus statistic that is more informative than any of the single measures.

Input: set of documents D, thresholds κ1, κ2 Output: set of probability models P Anchor(D) (1) P ← ∅ (2) W ← LDA n(D) (3) foreach i = 1... n (4)     L i ← { d ∈ D| w i(d) > κ1} (5)     L −i ← { d ∈ D| w i(d) ≤ κ2} (6)     p i ← train(L i, L −i) (7)     P ← P ∪ { p i} (8) return P or proportions and where line (6) can be replaced with any supervised method that uses the labels L i, i = 1... n.

In the TREC 2016 CDS task, Harsha Gurulingappa et al. [15] proposed a semi-supervised method that takes the advantages of pseudo-relevance feedback, semantic query expansion and document similarity measures based on unsupervised word embeddings.

This motivates us to employ the semi-supervised method that can utilize the information from the unlabeled samples to predict drug-protein interaction.

We have developed a novel semi-supervised method that exploits the relative enrichment and depletion of multiple signals from ChIP-Seq experiments to predict enhancers that are active in one cell line relative to another.

To circumvent this limitation, class prediction is best approached using supervised methods that extract information from a representative and randomly generated 'training' set under the guidance of a 'teacher', and then relate it to a 'test' set to confirm the validity of this extracted information.

There are a number of supervised methods that utilize gene expression data for the identification of genes and tumor classification.