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Support Vector Machines for classification

To whet your appetite for support vector machines, here’s a quote from machine learning researcher Andrew Ng:

“SVMs are among the best (and many believe are indeed the best) ‘off-the-shelf’ supervised learning algorithms.”

Andrew Ng

Professor Ng covers SVMs in his excellent Machine Learning MOOC, a gateway for many into the realm of data science, but leaves out some details, motivating us to put together some notes here to answer the question:

“What are the support vectors in support vector machines?”

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Traffic patterns of the year: 2014 edition

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As we mentioned in the last post, there are currently over 2000 active speed loop detectors within the Bay Area highway system.  The information provided by these loops is often highly redundant because speeds at neighboring sites typically differ little from one another.  This observation suggests that a higher level, “macro” picture of traffic conditions could provide more insight:  Rather than stating the speed at each detector, we might instead offer info like “101S is rather slow right now”.   In fact, we aim to characterize traffic conditions as efficiently as possible.  To move towards this goal, we have carried out a principal component analysis (PCA)$^1$ of the full 2014 (year to date) PEMS data set. (more…)

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