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https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-034-artificial-intelligence-fall-2010/lecture-videos/lecture-16-learning-support-vector-machines/
In this lecture, we explore support vector machines in some mathematical detail. We use Lagrange multipliers to maximize the width of the street given certain constraints. If needed, we transform vectors into another space, using a kernel function.
https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-867-machine-learning-fall-2006/lecture-notes/lec3.pdf
The Support Vector Machine ... Massachusetts Institute of Technology. Downloaded on [DD Month YYYY]. 6.867 Machine learning, lecture 3 (Jaakkola) 2 ... get the same classifier if we had only received the support vectors as training examples. Is this is a good thing? To answer this question we need a bit more formal (and fair) way of
http://web.mit.edu/6.034/wwwbob/svm.pdf
•Support vector machines Support Vectors again for linearly separable case •Support vectors are the elements of the training set that would change the position of the dividing hyperplane if removed. •Support vectors are the critical elements of the training set •The problem of …
http://www.mit.edu/~9.520/spring11/slides/class06-svm.pdf
Support Vector Machines Charlie Frogner 1 MIT 2011 1Slides mostly stolen from Ryan Rifkin (Google). C. Frogner Support Vector Machines. Plan Regularization derivation of SVMs. Analyzing the SVM problem: optimization, duality. Geometric derivation of SVMs. Practical issues.Cited by: 3145
https://en.wikipedia.org/wiki/Support-vector_machine
The support-vector clustering algorithm, created by Hava Siegelmann and Vladimir Vapnik, applies the statistics of support vectors, developed in the support vector machines algorithm, to categorize unlabeled data, and is one of the most widely used clustering algorithms in industrial applications. [citation needed
https://dspace.mit.edu/handle/1721.1/7290
The Support Vector Machine (SVM) is a new and very promising classification technique developed by Vapnik and his group at AT&T Bell Labs. This new learning algorithm can be seen as an alternative training technique for Polynomial, Radial Basis Function and Multi-Layer Perceptron classifiers.
https://direct.mit.edu/books/book/1821/Learning-with-KernelsSupport-Vector-Machines
Bernhard Schölkopf is Director at the Max Planck Institute for Intelligent Systems in Tübingen, Germany. He is coauthor of Learning with Kernels (2002) and is a coeditor of Advances in Kernel Methods: Support Vector Learning (1998), Advances in Large-Margin Classifiers (2000), and Kernel Methods in Computational Biology (2004), all published by the MIT Press.
https://ieeexplore.ieee.org/book/6267332/
In the 1990s, a new type of learning algorithm was developed, based on results from statistical learning theory: the Support Vector Machine (SVM). This gave rise to a new class of theoretically elegant learning machines that use a central concept of SVMs -- -kernels--for a number of learning tasks ...
https://mitpress.mit.edu/books/learning-kernels
A comprehensive introduction to Support Vector Machines and related kernel methods. In the 1990s, a new type of learning algorithm was developed, based on results from statistical learning theory: the Support Vector Machine (SVM). This gave rise to a new class of theoretically elegant learning machines that use a central concept of SVMs—-kernels—for a number of learning tasks. Kernel ...
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