The phrase "heavily supervised" is correct and usable in written English.
You can use this phrase to describe situations that require close monitoring or vigilance to ensure that tasks are done properly. For example, "Due to the high risk nature of the job, the training program was heavily supervised."
Such visits are heavily supervised, however, starting at the border.
At first, she said, the twins were in the same home, but Thomas had angry outbursts and had to be put in a more heavily supervised setting.
That is at odds with the view of those who believe companies should be run, or at least heavily supervised, by boards.
Across from where I fired my pistol, two black women, one with a small son, were taking turns (the child heavily supervised).
Two of the biggest miscreants were Fannie Mae and Freddie Mac, which, in theory, "were probably the world's most heavily supervised financial institutions," according to Jonathan Kay of The Financial Times.
So far, at least, a striking feature of the crisis has been that hedge funds, the least regulated part of the finance industry, have proved more stable than more heavily supervised institutions.Similarly, re-regulation should proceed cautiously and with an eye to unintended consequences.
He was removed many times from the dormitory cabin he shared with 14 other boys and put in a one-on-one supervised situation, often heavily monitored quarters in the infirmary.
Nowadays, the traditional supervised learning methods heavily rely on the classification accuracy of the training samples.
Consequently data sets for the training of supervised methods are heavily unbalanced, and this could have a negative impact on the prediction accuracy of the classifier.
While rule-based approaches use existing biomedical knowledge/resources, supervised machine learning based approaches rely heavily on annotated training data and domain dictionaries.
Most of existing studies either heavily rely on the expensive label information using the supervised learning or can hardly exploit the discriminative information borrowed from related domains.
