Abstract
Unlabeled data tells us how the instances from all the classes, mixed together, are distributed. If we know how the instances from each class are distributed, we may decompose the mixture into individual classes. This is the idea behind mixture models. In this chapter, we formalize the idea of mixture models for semi-supervised learning. First we review some concepts in probabilistic modeling. Readers familiar with machine learning can skip to Section 3.2.
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© 2009 Springer Nature Switzerland AG
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Zhu, X., Goldberg, A.B. (2009). Mixture Models and EM. In: Introduction to Semi-Supervised Learning. Synthesis Lectures on Artificial Intelligence and Machine Learning. Springer, Cham. https://doi.org/10.1007/978-3-031-01548-9_3
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DOI: https://doi.org/10.1007/978-3-031-01548-9_3
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-00420-9
Online ISBN: 978-3-031-01548-9
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