Training a model requires some information to guide it to a useful conclusion. The information is often in the form of human-domain knowledge, which mostly appears as labels. However, labels are not always available or in the format that we would wish for. Yingsong Zhang walks you through three situations to illustrate how to apply semisupervised learning to real problems:
Yingsong Zhang is a data scientist at ASI, where she has worked on everything from social media data to special data from clients to build predictive models. Yingsong has published over 10 first-author research papers in top journals and conferences in the field of signal/image processing and has accumulated extensive experience in algorithm design and information representation. She recently completed a three-year postdoc project at Imperial College London developing sampling theory and the application system. Yingsong holds a BA in mathematics, an MSc in artificial intelligence and pattern recognition from one of China’s top universities, and a PhD in signal and image processing from Cambridge University.
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