An article on Expectation Maximization Theory, taken from the book “Biometric Authentication: A Machine Learning Approach”.
The article/book-chapter addresses a data-clustering algorithm, called the expectation-maximization (EM) algorithm, when complete or partial information of observed data is made available.
The flow of the EM algorithm.
The book is written by M.W. Mak, S.Y. Kung, S.H. Lin. and the sample chapter is provided courtesy of Prentice Hall PTR. / Jan 3, 2005.

The book covers the following:
* How machine learning approaches differ from conventional template matching
* Theoretical pillars of machine learning for complex pattern recognition and classification
* Expectation-maximization (EM) algorithms and support vector machines (SVM)
* Multi-layer learning models and back-propagation (BP) algorithms
* Probabilistic decision-based neural networks (PDNNs) for face biometrics
* Flexible structural frameworks for incorporating machine learning subsystems in biometric applications
* Hierarchical mixture of experts and inter-class learning strategies based on class-based modular networks
* Multi-cue data fusion techniques that integrate face and voice recognition
Access the chapter on Expectation Maximization Theory online here.
Biometric Authentication: A Machine Learning Approach
* By S.Y. Kung, S.H. Lin, M.W. Mak.
* Published by Prentice Hall.
* Series: Prentice Hall Information and System Sciences Series.
ISBN: 0131478249; Published: Sep 14, 2004; Copyright 2005; Dimensions 7×9-1/4; Pages: 496; Edition: 1st.

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