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https://www.youtube.com/watch?v=Y6RRHw9uN9o
Aug 15, 2017 · A Support Vector Machine (SVM) is a discriminative classifier formally defined by a separating hyperplane. In other words, given labeled training data (supervised learning), the algorithm outputs ...Author: Augmented Startups
http://cs229.stanford.edu/notes/cs229-notes3.pdf
Support Vector Machines This set of notes presents the Support Vector Machine (SVM) learning al-gorithm. SVMs are among the best (and many believe are indeed the best) “off-the-shelf” supervised learning algorithms. To tell the SVM story, we’ll need to first talk about margins and the idea of separating data with a large “gap.”
https://scikit-learn.org/stable/modules/svm.html
Support Vector Machine algorithms are not scale invariant, so it is highly recommended to scale your data. For example, scale each attribute on the input vector X to [0,1] or [-1,+1], or standardize it to have mean 0 and variance 1. Note that the same scaling must be applied to
https://jakevdp.github.io/PythonDataScienceHandbook/05.07-support-vector-machines.html
Fitting a support vector machine¶ Let's see the result of an actual fit to this data: we will use Scikit-Learn's support vector classifier to train an SVM model on this data. For the time being, we will use a linear kernel and set the C parameter to a very large number (we'll discuss the meaning of …
https://towardsdatascience.com/support-vector-machines-svm-c9ef22815589
Oct 20, 2018 · Support Vector Machine are perhaps one of the most popular and talked about machine learning algorithms.They were extremely popular around the time they were developed in the 1990s and continue to be the go-to method for a high performing algorithm with little tuning. In this blog we will be mapping the various concepts of SVC.
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