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From von Neumann Architecture and Atanasoffs ABC to Neuro-Morphic Computation and Kasabov’s NeuCube: Principles and Implementations Read chapter Read first chapter

2018 | OriginalPaper | Chapter

Introduction to the Theory of Randomized Machine Learning

Authors : Yuri S. Popkov, Yuri A. Dubnov, Alexey Y. Popkov

Published in: Learning Systems: From Theory to Practice

Publisher: Springer International Publishing

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Abstract

We propose a new machine learning concept called Randomized Machine Learning, in which model parameters are assumed random and data are assumed to contain random errors. Distinction of this approach from “classical” machine learning is that optimal estimation deals with the probability density functions of random parameters and the “worst” probability density of random data errors. As the optimality criterion of estimation, randomized machine learning employs the generalized information entropy maximized on a set described by the system of empirical balances. We apply this approach to text classification and dynamic regression problems. The results illustrate capabilities of the approach.

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Footnotes
1
In particular, option transactions employ the mean values of financial tools having power dependence on random parameters [1].
 
2
This treatment differs from the classical definition of robustness given in [7].
 
3
Distribution of objects among n classes is reduced to \(C^2_n\) distributions among 2 classes of n.
 
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Metadata
Title
Introduction to the Theory of Randomized Machine Learning
Authors
Yuri S. Popkov
Yuri A. Dubnov
Alexey Y. Popkov
Copyright Year
2018
Book
Learning Systems: From Theory to Practice

Print ISBN: 978-3-319-75180-1

Electronic ISBN: 978-3-319-75181-8

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-75181-8_10

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