Sentence examples for in a supervised machine from inspiring English sources

Moreover, no suitable reference dataset is available to allow for method evaluation and method development in a supervised machine learning approach.

I evaluated the ability of the different histone modification data to identify known heart enhancers in a supervised machine learning framework.

In a supervised machine learning problem the user has to provide as input a reference library containing specimens with a priori known species membership.

These results show that the two proposed feature selection methods based on hexamer frequencies and random triplet-pair could be successfully incorporated into a supervised machine learning method in promoter classification problem.

The pipeline, represented in Figure  1, is a supervised machine learning workflow consisting in 3 main consecutive phases: case selection and tumour specimen processing, unbiased l 1 l 2 feature selection framework with functional characterization of the gene signature, and real-time quantitative reverse transcription-PCR (qPCR).

First pioneered by Vapnik in 1995, SVM is a supervised machine learning method which delivers state-of-the-art performance in recognition and discrimination of cryptic patterns in complex datasets [31].

Greevy & Smeaton [21] classified racist content in Web pages using a supervised machine learning approach with a bag-of-words (BOW) as features.

In this paper, we present a supervised machine learning approach that classifies seizure and nonseizure records using an open dataset containing 342 records.

In this paper we proposed a supervised machine learning approach to discover regulatory rules that can be used for constructing transcriptional regulatory networks.

In this study, we developed a supervised machine learning approach that was designed to recognize patterns from large and disparate data types, in order to identify candidate host-microbe interaction factors.

In this article, we suggest a supervised machine learning approach for the prediction of physical protein-protein interactions based on a collection of more than 100 different protein and gene features, using negative sets that preserve the degree of each protein as in the positive set.