Sentence examples for training conditional from inspiring English sources
The GPS is defined as the conditional probability of receiving a particular level of treatment 't' (hours of training) conditional on the same set of baseline covariates X used in section 6.1.
Training conditional relationships using a matching-to-sample (MTS) procedure typically consists of providing one sample, A1, and then rewarding only subsequent choices of B1 (and not, for example, B2).
Such influences include preferentially allocating funded local and international tuberculosis training places to eligible applicants from geographical regions with relatively high disease burden, and making fully-funded tuberculosis training conditional on trainees working in a tuberculosis high prevalence region for a specified period.
Their results showed the superior performance of the MCRF over HMMs and separately trained conditional random field.
Next, we train conditional random fields to predict chemical names in the articles, and the training patterns are extracted from the CTD.
While regression forests try to learn the probability over the parameter space from all face images in the training set, conditional regression forests learn instead several conditional probabilities over the parameter space, and thus can deal with facial variations in appearance and shape.
Training of conditional random forests is very similar to random forests; the main difference is that the probability of assigning a patch to a class is conditioned on the given head pose.
Eight adults with typical development were trained on conditional relationships between nonsense words that were composed of three letters.
On the other hand, NN-based approaches directly train the conditional probability, which converts the feature vector of a source speaker to that of a target speaker.
The drivers said in focus groups that the security threat from a suspicious item on the train was conditional on its being an actual explosive device the likelihood of which they rated as extremely low.
The method uses a positive dataset to train a conditional random field (CRF) model.
