Chapter 1: Machine learning cloud regression and optimization
The Swiss army knife of optimization
Abstract
This chapter is not about regression performed in the cloud. It is about considering your data set as a cloud of points or observations, where the concepts of dependent and independent variables (the response and the features) are blurred. It is a very general type of regression, offering backward-compatibility with existing methods. Treating a variable as the response amounts to setting a constraint on the multivariate parameter, and results in an optimization algorithm with Lagrange multipliers. The originality comes from unifying and bringing under the same umbrella a number of disparate methods, each solving a part of the general problem ...
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