Sentence examples for supervised technique that from inspiring English sources

LDA is a supervised technique that attempts to maximize the linear separability between data points (features) belonging to different classes (targeted modulation schemes) [30].

Previous approaches when faced with the dimensionality of the problem have tended to use unsupervised or supervised techniques that result in smaller clusters of genes, but clusters by themselves do not yield classification rules.

In this study, we are interested in supervised learning technique that finds the best described computer model from a dataset with the correct class variable.

In the domain of data mining [29], classification is a supervised learning technique that can be defined as follows: given a set of observations, we are interested in extracting certain rules that can be used to predict the class of the each new observation.

SVM is a supervised learning technique that was originally developed as a classifier.

The creation of a supervised classification technique that exploits the intuitive nature of Markov mixture models would be a powerful interpretable tool for biologists to analyze network pathways.

Support vector machine is a sophisticated supervised machine learning technique that is built based on statistical learning theory [59], [60] and has been widely used in the applications of bioinformatics.

Support Vector Machine is a supervised machine learning technique that is widely used in pattern recognition and classification problems.

Support Vector Machine (SVM) is a supervised machine learning technique that is widely used in pattern recognition and classification problems.

Efforts have been made to alleviate this acute problem, in particular, by using supervised learning techniques that allow to group pairs of nodes in categories for link prediction and, therefore, reduce the imbalance effect [11].

A characteristic of supervised techniques is that clinical prediction rules based on the results of such research are usually dependent on a single outcome and therefore this type of research may lead to a proliferation of competing prediction rules.