As expected, the results shows that with a small semi-supervision, the accuracy is ruled by the chosen samples, and as the number of active samples increases, the variance decreases.
The availability of advanced electronic components of high accuracy and microcontroller supervision enables to build a simple, reliable and not expensive device for use in the range water potential from −6 up to −0.2 MPa and possibly higher.
It shows the results for 1, 10 and (50~%) of initial supervision using both evaluation metrics: Accuracy and maxF1.
We show that Sma3s can rapidly produce high levels of prediction accuracy with minimal human supervision and modest computational resources.
The system calculates accuracy using the distant supervision training labels, and thus this calibration represents DeepDive's performance based on the provided labels.
On two standard semantic parsing benchmarks (GEO and JOBS), our system obtains the highest published accuracies, despite using less supervision than existing systems.
Formalization of a mHealth M&E system for PHC outreach teams delivering community based services could offer greater accuracy of M&E and enhance supervision systems for CHWs.
Due the large intraclass variance, the SVM obtains a better accuracy only until (5~%) of supervision, after it, the semi-supervised algorithm obtains a better results.
Only for (50~%) of semi-supervision the presented approach obtains a better accuracy, while the active learning has similar results to the ones obtained by SVM.
The first one, on cyan, (92~%) of accuracy is obtained with a small supervision, while, on the second one, on red, only (86~%) of accuracy is obtained.
