Sentence examples for supervised framework from inspiring English sources

The notion of relevance can be cast into a supervised framework by considering that for each one of the x ij features belonging to the feature subset, the relevance function ρ is defined as follows [1]: ρ : R F × T × K → R F × T ( X, C, x ij ) ↦ ρ ( X, C, x ij ) ∈ R + (3).

Following a strategy similar to [17], we combine these rankings in a supervised framework to draw as much information as possible from these features, so that the resulting ranking should rank high the pairs which are most likely to be connected.

Employing the supervised framework, we evaluate the predictive performance of classifiers derived from cancer-specific datasets, a cancer specific compendium, and a human cancer compendium.

We have extended the supervised framework of Yeung et al. [ 3] in three ways.

For each gene g, rank the candidate regulators based on the regulatory potentials predicted from the supervised framework.

While the supervised framework was shown to enrich current PPI data with additional inferred PPIs, its applicability is still limited.

Galar et al. [ 13] showed that, in supervised frameworks, ensembles perform better than single learners trained on resampled data.

We summarize the proposed semi-supervised framework in Algorithm 1.

The proposed CCLS approach integrates the LS and CS techniques under a unified semi-supervised framework, with two additional improvements: more accurate estimation of local structure and variance.

Optimal reverse prediction (ORP) has recently been proposed as a semi-supervised framework to unify supervised and unsupervised training methods such as supervised least square, principal component analysis (PCA), k-means clustering and normalized graph-cut.

For unsupervised learning, k-means clustering, training and classification procedures for supervised and semi-supervised framework are implemented using the MATLAB® (Natick, MA).