Support Vector Machines (SVM)


Given a set of training examples, each marked as belonging to one or the other of two categories, an SVM training algorithm builds a model that assigns new examples to one category or the other, making it a non-probabilistic binary linear classifier.

Overview

Support Vector Machines
SVMs work well in complicated feature domains, albeit requiring clear separation between classes.
support-vector-machines tutorial article

Tutorials

A Basic Soft-Margin Kernel SVM Implementation in Python
This is a basic implementation of a soft-margin kernel SVM solver in Python using numpy and cvxopt.
support-vector-machines python tutorial article
Linear classification: Support Vector Machine, Softmax
The SVM loss is set up so that the SVM “wants” the correct class for each image to a have a score higher than the incorrect classes by some fixed margin Δ.
support-vector-machines cs231n stanford tutorial

Libraries

ThunderSVM: A Fast SVM Library on GPUs and CPUs
Exploits GPUs and multi-core CPUs to achieve high efficiency with SVMs.
support-vector-machines gpu library code
Scikit-learn
Examples for all the different utilities within scikit-learn.
scikit-learn naive-bayes linear-regression logistic-regression
Fast Support Vector Classification with RAPIDS cuML
In this post, we will discuss how you can use the SVM package in RAPIDS cuML to perform fast support vector classification on a GPU.
support-vector-machines rapidsai nvidia cuml
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