Abstract
Unsupervised learning is a class of problems in machine learning where the goal is to determine how data is structured and organized. It is distinguished from supervised learning, semi-supervised learning and reinforcement learning in that the learner is given only unlabeled data. Unsupervised learning is closely related to the problem of density estimation in statistics. However unsupervised learning also encompasses many other techniques that seek to summarize and explain key features of the data[1]. In this section, we will mainly discuss two sorts of unsupervised learning models based on label semantics, probability estimation and clustering.
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© 2014 Zhejiang University Press, Hangzhou and Springer-Verlag Berlin Heidelberg
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Qin, Z., Tang, Y. (2014). Unsupervised Learning with Label Semantics. In: Uncertainty Modeling for Data Mining. Advanced Topics in Science and Technology in China. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41251-6_7
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DOI: https://doi.org/10.1007/978-3-642-41251-6_7
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