Unlabeled data partially alleviates the training data scarcity problem, thus improves the efficiency of support vector machine (SVM) active learning.
At present, the availability of newly trained national specialists is increasing, notwithstanding the shortage of young MDs and the scarcity of training hospital capacity for all but the most basic specialities.
Incidental zeros, could refer to people who looked for a dual VET place at a later stage, typically after doing something else for the first 2 years, and could not find one, for example because of the scarcity of training places that exists in some sectors (SERI 2015).
As the number of such interactions is small, there is a scarcity of training data.
However, when there is a scarcity of training data, the high flexibility could turn out to be a disadvantage, as there is a high risk of overfitting, i.e. the construction of overly complex models that fit the training data well but do not represent the general trend of the whole network.
Many factors are at play: complexities of transferring between decentralized units, or from the decentralized to the national level, fractured or non-existent information channels about job opportunities, skill levels that are getting outdated because of the scarcity of training opportunities, etc.
As for external funds, an issue of year-on-year availability was identified, rather than one of absolute scarcity, as training expenditures were anticipated to cumulate during the first few years, leaving a financing gap that could be closed only in 2012.
This low performance was due to the lack of features tailored to specific PHI types, as well as to the scarcity of training examples in a few PHI types (e.g., only 4 examples of Zip code and Age > 89 in our evaluation corpus).
This scarcity in training together with a work context where too often protocols for the management of victims of violence are nonexistent - protocols which should be established, shared and implemented by the senior managers and chief physicians of the facilities [ 8- 10].
Mental health research capacity and infrastructure in Africa are extremely limited, with little dedicated funding, a scarcity of trained mental health research personnel, a dearth of infrastructural support, and few opportunities for training, supervision and career development in mental health research (Thornicroft et al. 2012).
