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We are using ML to build a compiler that does low-level optimization.
Existing verification projects have mostly combined crowdsourcing with ML to build verification projects.
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Taking this into account, it is possible to exploit this correlation using machine learning (ML) techniques [30] to build a decision tree which decides the SVC decision mode depending on the values of certain information extracted from the H.264/AVC decoding stage.
The ML method serves to build on the logistic regression method as an improved tool, rather than a replacement of logistic regression.
The importance of understanding the needs of local populations should also not be overlooked or underestimated, a capacity that the MLs had begun to build quite successfully.
In this study, a machine learning (ML) based intelligent scoring system [ 17] (subsequently referred to here as the ML score) was implemented to build prediction models.
Neighbor Join (NJ) and Maximum Likelihood (ML) methods were used to build phylogenetic trees for each of the 968 clusters (Additional files 1 and 2).
To infer phylogenetic relationships from genes selected for further discussion (i.e., dsbA), protein sequences were aligned with MUSCLE and ML method was used to build the trees under the Jones–Taylor Thornton substitutions model.
The Phylip package ver. 3.6 was used to build the ML trees with boot strapping values.
Such methods include supervised machine learning (ML) techniques, which are used to build classification models.
TREEFINDER was used to build the ML phylogeny, while BEAST[ 43] was used to reconstruct the Ba phylogeny.
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