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https://en.wikipedia.org/wiki/Support-vector_machine
Support-vector machine weights have also been used to interpret SVM models in the past. Posthoc interpretation of support-vector machine models in order to identify features used by the model to make predictions is a relatively new area of research with special significance in the biological sciences. History
https://towardsdatascience.com/understanding-support-vector-machine-part-2-kernel-trick-mercers-theorem-e1e6848c6c4d
Dec 19, 2018 · Prerequisite: 1. Knowledge of Support vector machine algorithm which I have discussed in the previous post.2. Some basic knowledge of algebra. In the 1st part of this series, from the mathematical formulation of support vectors, we have found two important concepts of SVM, which areAuthor: Saptashwa Bhattacharyya
https://www.youtube.com/watch?v=vMmG_7JcfIc
Oct 05, 2017 · In machine learning, kernel methods are a class of algorithms for pattern analysis, whose best known member is the support vector machine (SVM). The general task of pattern analysis is to find and ...Author: el mustapha ben bihi
https://www.coursera.org/lecture/machine-learning/kernels-i-YOMHn
Video created by Stanford University for the course "Machine Learning". Support vector machines, or SVMs, is a machine learning algorithm for classification. We introduce the idea and intuitions behind SVMs and discuss how to use it in practice.
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