Half of these data were used for supervised model adaptation.
However, supervised models require available inputs, and missing inputs affect the inferring process in the output.
The pure supervised model can construct a very accurate predictive model from data streams.
The results show that unlabeled data results in improvement in classification accuracy over the supervised model.
And while supervised models allow for more control over bias in data selection, that control can introduce human bias into the process.
Two case studies are provided for performance evaluation of the proposed method, both in comparison with the conventional supervised model.
We studied theoretically matrix factorization by turning it from a trainable supervised model into a generative probabilistic model.
We train a supervised model using neural network and backpropagation algorithm for classifying users and reviews as fraud and genuine.
The aim of this study is to propose a supervised model to detect mitosis signature from breast histopathology WSI images.
First, while typed dependency examples improved classification in the majority of the supervised models, they also illustrated how specific predictive terms are related to event-related contexts.
