Sentence examples for supervised methods were from inspiring English sources

Recently, saliency detection has become an active research topic in learning from labeled image, where various supervised methods were designed.

A lack of a gold standard gene-disease-chemical network is also why no semi-supervised or supervised methods were chosen.

Supervised methods were used to discover expression patterns associated with the length of survival in a training set of 95 specimens.

The main disadvantage of these supervised methods is their dependency on labeling data.

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

More advanced supervised methods are likely to improve predictions, but developing techniques to infer data of non-users carries important ethical issues that need to be addressed before such research is performed.

examples of the supervised methods are maximum margin criteria (MMC), maximum relevance minimum redundancy (mRMR) and linear discriminate analysis (LDA), (ii) the unsupervised technique, however, transform the existing features by rotating and projecting them onto a minimal number of axes without using the target labels.

One of the few comparisons with supervised methods was performed by Mordelet and Vert [ 9].

Although these supervised methods are effective, they require manually curated training data sets.

Validation of supervised methods is vital to test model stability, and we have employed the following procedure before [ 17].

The main goal of supervised methods is to infer a classification function from a labeled training dataset.