Classifiers Sample Clauses

Classifiers. Employees seeking to be appointed by the Province as classifiers shall have their applications co-signed by the Employer. Subject to operational requirements employees offered such assignments by the Province will be granted leave without pay. On the basis that the Employer will be fully reimbursed for any such leave by the Ministry of Health, the Employer will maintain the employee’s regular straight time wages and will provide full accumulation of seniority and service and as well as all other benefits under the collective agreement. If such leave is not fully funded by the Ministry of Health, it shall be without pay and subject to the effect of absence language.

Classifiers. All classifiers used the same architecture (Fig. 2) and hyperparameters. The input size to the classifiers were 21x60 (channels x time). Each classifier started with five convolutional layers; each layer was followed by a scaled-exponential rectifying unit (SELU) (Xxxxxxxxx et al., 2017), and a max pooling layer which downsampled by a factor of two. Filter kernel sizes were three and the number of filters per layer were 32, 64, 128, 256, and 512. Channels were xxx- lyzed separately. Thereafter, four fully connected layers followed, the three first having 512 nodes. SELU activations was used for all layers except the last, which had one node and a sig- moid activation to complete the classifier. The total number of trainable parameters was 6,640,769.

Classifiers. For the classifiers to identify roughly the same number of EDs as their respective expert anno- tation they were trained on, the thresholds for identification based on the sigmoid output were adjusted to 0.48, 0.45, 0.35, and 0.55. The performances as assessed by the metrics were moderate in most instances (Tab. 1): B-ACC 0.76–0.82, R-AUC 0.52–0.72, PR-AUC 0.51–0.65, F0.5 0.53–0.75, MCC 0.51–0.72, sensitivity 0.54–0.75, and precision 0.55–0.75. Most of the EEG was non-ED, and this was reflected in higher scores for ACC 0.94–0.96, specificity 0.97–0.98, and NPV 0.97-0.98. The best overall result was obtained for classifier U. The metrics were also calculated comparing the experts using expert 1 as ground truth. In comparison to the classifiers most values were somewhat lower for the experts. 1. Results for the classifiers. Values are averages over folds (±standard deviation). E12: using expert 1 as ground truth and comparing with expert 2; E1: expert 1; E2: expert 2; U: union of expert 1 and 2; I: intersection of expert 1 and 2; Data: indicate which annotation that was used for training and evaluation; ACC: accuracy; B-ACC: balanced accuracy; R-AUC: area under the curve based on receiver operating characteristic; PR-AUC: precision-recall area under the curve; MCC: Xxxxxxx’s correlation coefficient; Sens: sensitivity; Spec: specificity; Prec: precision; NPV: negative predictive value. Blue indicates comparison between expert 1 and 2; green indicates results from testing the classifiers; light green indicates results from training the classifiers. Data ACC B-ACC R-AUC PR-AUC F0.5 MCC Sens Spec Prec NPV

Classifiers. A typology of noun categorization devices. Oxford: Oxford University Press.

Classifiers. Once the feature space is defined and optionally reduced, each classifier (classification algorithm) builds a normalized weighted structure for each category. This structure is either represented as a vector in the feature space (for centroid classifier) or as a probability distribution (for naive bayesian and entropy classifiers). The categorization model is actually a serialized version of the set of all weighted structures. Firstly, the i-Publisher user decides on the model feature type and dimension reduction techniques. Secondly, the i-Publisher backend sends the request for building a specific model to an input queue. The ACT then checks out the message and builds a model for each of the available/ configured classifiers (algorithms). This process fetches the training data, builds the feature space, reduces it, forms the vectors for each category and normalizes them. The set of vectors is then serialized and sent to an output queue. Finally, i-Publisher fetches the built model from the output queue, saves it in the database and marks the model as “ready-to-be-used”. The schema below outlines the main steps in the building of a categorization model. It is important to note that each classifier uses its own model. The process of using a model consists of the following steps: