Machine Learning | Semi-Supervised SVM (S3VM/TSVM)

S3VM stands for semi-supervised support vector machines. Semi-supervised machine learning is a class of machine learning that you have some labeled data sets and also an amount of unlabeled data. The semi-supervised learning approach is motivated by the fact that it is easier (and cheaper) to collect unlabeled training examples. For example, it is easy to crawl the web and collect images but it is much more difficult to do with their associated labels. As far as S3VM go, you start with the SVM formulation and then modify the constraints of the optimization problem to formulate an S3VM. #S3SVM #TSVM #S3VM
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Chapters:
⏱️ 0:00 Idea of S3VM
⏱️ 5:37 SVM vs. S3VM
⏱️ 6:35 Hinge loss & Hat loss function
⏱️ 9:39 Assumptions of S3VM
⏱️ 11:34 Problems to be addressed
⏱️ 12:30 Advantages
⏱️ 13:14 Disadvantages
⏱️ 14:56 SVM-light
⏱️ 15:50 Label Switching
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