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blog.omega-prime.co.uk
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| | | | | The most fundamental technique in statistical learning is ordinary least squares (OLS) regression. If we have a vector of observations \(y\) and a matrix of features associated with each observation \(X\), then we assume the observations are a linear function of the features plus some (iid) random noise, \(\epsilon\): | |
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www.jeremykun.com
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| | | | | This post is a sequel to Formulating the Support Vector Machine Optimization Problem. The Karush-Kuhn-Tucker theorem Generic optimization problems are hard to solve efficiently. However, optimization problems whose objective and constraints have special structure often succumb to analytic simplifications. For example, if you want to optimize a linear function subject to linear equality constraints, one can compute the Lagrangian of the system and find the zeros of its gradient. More generally, optimizing... | |
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matbesancon.xyz
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| | | | | Learning by doing: predicting the outcome. | |
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d2l.ai
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