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A novel OCC method for human action recognition namely the Laplacian One Class Extreme Learning Machines is presented. The proposed method exploits local geometric data information within the OC-ELM optimization process. It is shown that emphasizing on preserving the local geometry of the data leads to a regularized solution, which models the target class more efficiently than the standard OC-ELM...
Timely reporting and analysis of adverse events and medical errors is critical to driving forward programs in patient-safety, however, due to the large numbers of event reports accumulating daily in health institutions, manually finding and labeling certain types of errors or events is becoming increasingly challenging. We propose to automatically classify/label event reports via semi-supervised learning...
Co-training is one promising paradigm of semi-supervised learning and has drawn considerable attentions and interests in recent years. It usually works in an iterative manner on two disjoint view features, in which two classifiers are trained on the different views and teach each other by adding the predictions of unlabeled data to the training set of the other view. However, the classifier performs...
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