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We believe that wherever feasible these data should be fully compliant with global data standards, machine-readable, and (while respecting legitimate concerns for privacy and for the protection of endangered species) accessible without restriction to the scientific community [ 10].
The most popular approach is to buy a standard machine of the sort sold in large volumes by big companies such as Lenovo, HP, Dell, Asus and Acer.
This data scarcity might render standard machine learning algorithms inapplicable.
We compare the method with a standard machine learning approach based on nearest neighbour.
To find evidence of human settlement in the images, the team used standard machine learning techniques.
A new force standard machine (FSM) for the range of 100 μN–100 mN has been developed [1].
NeuCube models result in a better accuracy of STBD classification than standard machine learning techniques.
The proposed architecture is evaluated on the basis of two standard machine learning tasks.
We have experimented with two standard machine learning algorithms for classification: Naïve Bayes (NB) and Support Vector Machine (SVM) classifiers.
Finally, a complex application is described, in which a force standard machine combines four of these measurement principles.
Stanford Recursive Deep Model [41] and SentiStrength [11] were both compared with standard machine learning approaches, with their own datasets.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com