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Traditionally, algorithmic and machine learning techniques have been used to analyse company filings, profit margins, stock prices, px ratios etc. to try to gain a competitive advantage, but it has become increasingly on trend to analyse social data in the same way we look at numbers and figures.
The machine learning techniques used here can be separated into three stages: algorithmic selection of a small set of voxels believed to be useful for classification; training of a classifier on a subset of the data; and finally testing of the classifier on an independent subset of the data.
Although there are many periodic extensions for many basic algorithmic problems in the literature, there are not many data mining or machine learning techniques specifically proposed for them.
In summary, we have presented an algorithmic approach that provides a critical advance in applying computer vision and machine learning techniques as a tool for clinical geneticists.
One of our ideas to soon improve our pipeline from an algorithmic point of view is to, instead of relying to fixed parameters, base the parameter optimization onto machine learning techniques, i.e. let a support vector machines select the optimal parameters.
Learning techniques is the key.
Three learning techniques were investigated.
Hence, a number of algorithmic approaches have been developed over the past decade for the prediction of phenotype from genotype [ 6- 10], including models trained using statistical machine learning techniques [ 11- 13].
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CEO of Professional Science Editing for Scientists @ prosciediting.com