Abstract
In this chapter we do violence to some problems to reveal their inner structure. The focus is on problems which, at first glance, may not seem to be of the continuous linear variety yet can be marshalled into that form with a handful of creative alterations. The key is to ensure a one-to-one correspondence between the original and the altered problems so that we can retrieve a solution to the original from a solution to the alteration.
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Notes
- 1.
All research mathematicians agree on the labels “convex” and its opposite, “concave,” but textbook authors for high schools in the US, ignoring thousands of papers, journals, and research monographs, insist on “concave up” and “concave down.”
- 2.
The expression “regression” comes from Francis Galton’s original paper about “Re-gression towards mediocrity” and shadows rather than highlights the technique. As for “parameter,” what, pray tell, is not a parameter?
- 3.
Carl Friedrich Gauss, Theoria Combinationis Observationum Erroribus Minimis Obnoxiae (Theory of the Combination of Observations Least Subject to Errors) (Philadelphia, PA: Society for Industrial and Applied Mathematics, 1987).
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© 2018 Serge Kruk
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Kruk, S. (2018). Hidden Linear Continuous Models. In: Practical Python AI Projects. Apress, Berkeley, CA. https://doi.org/10.1007/978-1-4842-3423-5_3
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DOI: https://doi.org/10.1007/978-1-4842-3423-5_3
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Publisher Name: Apress, Berkeley, CA
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