Abstract
The remainder of the book provides machine learning examples in MATLAB that span the technologies discussed. Each example provides a useful application in its own right. Full source code is provided. In each case the theory behind the code is provided. References for further study are provided. Each example is self-contained and addresses one of the autonomous learning technologies discussed earlier in the book. You can jump around and try the examples that interest you the most.
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References
S. Sarkka. Lecture 3: Bayesian Optimal Filtering Equations and the Kalman Filter. Technical report, Department of Biomedical Engineering and Computational Science, Aalto University School of Science, February 2011.
M. C. VanDyke, J. L. Schwartz, and C. D. Hall. Unscented Kalman filtering for spacecraft attitude state and parameter estimation. Advances in Astronautical Sciences, 2005.
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© 2017 Michael Paluszek, Stephanie Thomas
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Paluszek, M., Thomas, S. (2017). Kalman Filters. In: MATLAB Machine Learning. Apress, Berkeley, CA. https://doi.org/10.1007/978-1-4842-2250-8_10
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DOI: https://doi.org/10.1007/978-1-4842-2250-8_10
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Publisher Name: Apress, Berkeley, CA
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Online ISBN: 978-1-4842-2250-8
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