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Conference Paper

#### Machine Learning Methods For Estimating Operator Equations

##### External Resource

https://www.sciencedirect.com/science/article/pii/S1474667015354288

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##### Fulltext (public)

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##### Supplementary Material (public)

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##### Citation

Steinke, F., & Schölkopf, B. (2006). Machine Learning Methods For Estimating Operator
Equations. In *IFAC Proceedings Volumes* (pp. 1192-1197). Oxford, United Kingdom: Elsevier.

Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-D27F-B

##### Abstract

We consider the problem of fitting a linear operator induced equation to point sampled data. In order to do so we systematically exploit the duality
between minimizing a regularization functional derived from an operator and
kernel regression methods. Standard machine learning model selection algorithms
can then be interpreted as a search of the equation best fitting given data points.
For many kernels this operator induced equation is a linear differential equation.
Thus, we link a continuous-time system identification task with common machine
learning methods.
The presented link opens up a wide variety of methods to be applied to this system
identification problem. In a series of experiments we demonstrate an example
algorithm working on non-uniformly spaced data, giving special focus to the
problem of identifying one system from multiple data recordings.