Sentence examples for machine learning combination from inspiring English sources

Nevertheless, we underline that even in aNobii's more challenging setting the relative improvement of machine learning combination of different profile and structural features over the performance of common neighbors is comparable to the improvement obtained in the case of Facebook by previous work.

In this paper, we utilize multi-agent machine learning and classifier combination to learn rock facies sequences from wireline well log data.

In machine learning, advanced model combination can be facilitated by ensemble learning according to Dietterich (2000) and Yao and Islam (2008), which comprises strategies for training and combining outputs of a number of machine learning algorithms.

However, the performances in the validation sets were poorer and they failed to outperform consistently the simplest model, i.e. AURKA, in spite of the use of machine learning strategies (namely combination of univariate models or regularization) to reduce the risk of overfitting.

We have previously developed a method for discovering transcriptional modules that uses rule-based machine learning to find combinations of motifs that are predictive of co-expression [ 29- 31].

Our goal is to develop a gene/protein name recognition system BioTagger-GM that exploits rich information in terminology sources using powerful machine learning frameworks and system combination.

NN-align performs especially well in handling large dataset among all other machine learning methods and in combination with ARB outperforms the use of NN-align alone [ 30].

Adaptive approaches based on machine learning would allow the combination of evidence types to be optimised for each dataset, although potentially at the expense of understanding specific features of the evidence in each case.

A new way to select vaccine targets and reduce the number of necessary experiments is based on the use of machine learning (ML) algorithms in combination with classical experimentation.

Current applications of personalization using human curation, algorithmic systems and machine learning methods — or their combinations — don't yet learn or deliver fast enough, nor do they let us express ourselves as unique individuals.

Utilizing random forests, an ensemble machine learning approach, we determined combinations of significantly associated miRs that contributed to the expression fold change of the underlying targets.