Sentence examples for methods of supervised from inspiring English sources

Present studies are focused toward extending this classification scheme to identify all subtypes of tissue composed with an excised lymph node, which has been successfully accomplished in several different organ models using different methods of supervised analysis [ 30, 37].

Recruitment and baseline data will be used to develop statistical models that relate the independent variables to the dependents: Multimorbidity patterns of similar severity will be identified by methods of supervised learning (methods of recursive partitioning like CART [Classification and Regression Trees], logistic regression).

Hence, SC-BLSOM is thought to be a kind of hybrid method of supervised and unsupervised learning because phylotype information is added in dividing the species-known genomic sequences into subclasses.

Unlike unsupervised methods, the class of supervised methods needs reference information.

Recently, it has repeatedly come to our attention while reading or reviewing manuscripts submitted to Bioinformatics and other journals that often no proper external evaluations of the whole method consisting of supervised feature selection and subsequent classification is provided.

The prediction performance is compared with six popular published methods for supervised discovery of motifs/CRMs based on a wide spectrum of models: Cister (Frith et al., 2002), Cluster-Buster (Frith et al., 2003), BayCis (Lin et al., 2008), Stubb (Sinha et al., 2006), Ahab (Rajewsky et al., 2002) and MSCAN (Johansson et al., 2003).

Next, the multilayer stacking method of the supervised AE is designed to build the supervised deep AE, since the deep network has more abundant and diversified features.

For direct comparison, we included two unsupervised methods (Z-SCORE, SPEARMAN) in our evaluation of supervised methods.

This paper addresses a new method for combination of supervised learning and reinforcement learning (RL).

An advantage of supervised methods is their ability to adapt and create trained models for specific purposes and contexts.

These prior models are in fact equivalent to a particular penalty function in Sanderson's approach [10], so that both of these approaches are often referred to as "regularization methods" in the literature of supervised learning.