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Our code also allows users to implement supervised learning approaches with their own representative labeled training data.
A thorough study of supervised learning approaches for deceptive review detection was conducted by Mukherjee et al. [23].
Inherent to supervised learning approaches is the limitation that these only utilize the evaluated information, i.e. the information from the samples whose performance functions have been computed.
To make this happen, the article introduced a different strategy to clustering techniques based on a fusion of unsupervised and supervised learning approaches.
Almost all software fault prediction studies use metrics and faulty data of previous software release to build fault prediction models, which is called supervised learning approaches.
Many other supervised learning approaches have been proposed but they all suffer from poor scalability due to the manual labeling involved in the training process.
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The machine learning-based engine is a supervised learning approach, whose outputs are dependent on the training samples, and hence has a relatively low coverage of predictions.
Figure 1 shows an overview of this supervised learning approach.
Functional recognition imaging is an example of the supervised learning approach that employs artificial neural networks.
Han et al. [19] implemented the supervised learning approach based on DNNs to perform speech dereverberation.
Methodologically, sentiment detection is typically conducted using a supervised learning approach.
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