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fa.bianp.net | ||
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nhigham.com
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| | | | | A real $latex n\times n$ matrix $LATEX A$ is symmetric positive definite if it is symmetric ($LATEX A$ is equal to its transpose, $LATEX A^T$) and $latex x^T\!Ax > 0 \quad \mbox{for all nonzero vectors}~x. $ By making particular choices of $latex x$ in this definition we can derive the inequalities $latex \begin{alignedat}{2} a_{ii} &>0... | |
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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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jeremykun.wordpress.com
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| | | | | This post is a sequel toFormulating 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 structureoften succumb to analytic simplifications. For example, if you want to optimize a linear function subject to linear equality constraints, one can compute... | |
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4gravitons.com
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| | | Fellow science communicators, think you can explain everything that goes on in your field? If so, I have a challenge for you. Pick a day, and go through all the new papers on arXiv.org in a single area. For each one, try to give a general-audience explanation of what the paper is about. To make... | ||
