Sentence examples for a set of supervised from inspiring English sources

The primary contribution is a set of supervised machine classification models for multiple protected characteristics - race, disability and sexual orientation - to complement the existing classifier for religion.

The last step (line 5) defines a set of supervised recommendations, which are performed based on the images labeled by the user.

Today we're looking at Philadelphia's plan to be the first US city with a set of supervised drug injection sites.

A crucial feature of the resource is a set of supervised machine learning algorithms for binary classification executed via Rscript.

Polymorphism Phenotyping-2 or PolyPhen-2 [ 11] which is a probabilistic classifier that computes functional impact of an allele change by Naive Bayes, a set of supervised learning algorithms, was used to determine structural consequences.

Two alternative approaches were developed: 1) a machine learning approach, where detailed features were extracted from the death certificate and were used to train a set of supervised classifiers; and 2) a set of keyword-spotting rules.

In fact, all pairwise distances among labeled images (relevant and non-relevant) are updated according to a single factor λ. Notice that the supervised approach also exploits information provided by the set of non-relevant images, defining a set of negative supervised recommendations (line 11 of the Algorithm ??).

Another work of interest was proposed by Xu et al. [21], which uses a restrict set of supervised data classified with a SVM classifier, using non-ranked data to improve the learning.

The pattern generation module uses a small set of supervised patterns to automatically search and build a large and comprehensive pattern set.

In this sense, it includes a wide set of supervised learning algorithms and is able to cope with complex fuzzy systems.

The originality of the proposed method lies in the formalization of bipartite graph inference as a set of independent local supervised learning problems, each of which predicts which new drug candidate compounds (respectively, new target candidate proteins) are connected to each target protein (respectively, each compound) in the training set.