Examples of the application semi-Supervised learning
Semi-supervised learning models are becoming widely applicable in a plethora of fields (Rodrigues, 2017). Below are some examples of scenarios that require intensive human intervention and where SSL techniques can make a big difference.
1. Medical Image Recognition and Analysis
In medical image analysis or computer-aided diagnosis (CAD), it is cheap and easy to have scanned images of the patients, however it is expensive to label them. The labeling process requires an expert such as a physician or radiologist to highlight the abnormal areas. (Shaaban et al, 2017)
2. Protein Sequence Classification and Prediction
Inferring the function of proteins and recognizing the (3D) structure or of a single protein requires months and expert annotators. SSL techniques such as cluster kernels (Shaaban et al, 2017, Weston et al, 2015) have proved to be successful.
3. Speech Recognition and Analysis
Labeling audio files is typically a very intensive task that requires a lot of human resources and applying SSL techniques can help to improve traditional speech analytic models.
4. Web Content Classification, Aggregation and Crawling engines
Organizing the knowledge available in billions of web pages will advance different segments of AI. Unfortunately, that task typically requires human intervention to classify and aggregate the content.
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