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Machine Learning for Signal ProcessingData Science, Algorithms, and Computational Statistics$
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Max A. Little

Print publication date: 2019

Print ISBN-13: 9780198714934

Published to Oxford Scholarship Online: October 2019

DOI: 10.1093/oso/9780198714934.001.0001

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Statistical machine learning

Statistical machine learning

Chapter:
(p.149) 6 Statistical machine learning
Source:
Machine Learning for Signal Processing
Author(s):

Max A. Little

Publisher:
Oxford University Press
DOI:10.1093/oso/9780198714934.003.0006

This chapter describes in detail how the main techniques of statistical machine learning can be constructed from the components described in earlier chapters. It presents these concepts in a way which demonstrates how these techniques can be viewed as special cases of a more general probabilistic model which we fit to some data.

Keywords:   Feature and kernel functions, mixture modelling, expectation-maximization, clustering, K-means, classification, linear and quadratic discriminant analysis, support vector machines, regression, Bayesian regression, nonparametric regression, dimensionality reduction, probabilistic principal components analysis

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