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https://gist.github.com/mblondel/586753
Sep 28, 2019 · Support Vector Machines. GitHub Gist: instantly share code, notes, and snippets.
https://github.com/the-ethan-hunt/awesome-svm
Nov 23, 2017 · Gist - Gist is a C implementation of support vector machine classification and kernel principal components analysis. SVMsequel - SVM multi-class classification package, distributed as binaries for Linux or Solaris. Kernels: linear, polynomial, radial basis function, sigmoid, string, tree, information diffusion on discrete manifolds. C++
https://www.igi-global.com/chapter/classification-gis-using-support-vector/20393
Classification in GIS Using Support Vector Machines: 10.4018/978-1-59140-995-3.ch014: Support Vector Machines (SVM) are powerful tools for classification of data. This article describes the functionality of SVM including their design andAuthor: Alina Lazar, Bradley A. Shellito
http://support-vector-machines.org/SVM_soft.html
SVM, support vector machines, SVMC, support vector machines classification, SVMR, support vector machines regression, kernel, machine learning, pattern recognition, cheminformatics, computational chemistry, bioinformatics, computational biology ... Gist is a C implementation of support vector machine classification and kernel principal ...
http://www.pybloggers.com/2016/02/using-support-vector-machines-for-digit-recognition/
The solution to this is to train multiple Support Vector Machines, that solve problems stated in this format: “Is this digit a 3 or not a 3?”. Now we are solving a binary classification again with the two classes “is a 3” and “is not a 3”. In our case we have one Support Vector Machine …
https://towardsdatascience.com/support-vector-machine-introduction-to-machine-learning-algorithms-934a444fca47
Jun 07, 2018 · Support vector machine is another simple algorithm that every machine learning expert should have in his/her arsenal. Support vector machine is highly preferred by many as it produces significant accuracy with less computation power. Support Vector Machine, abbreviated as SVM can be used for both regression and classification tasks.
http://people.ischool.berkeley.edu/~hearst/papers/ieee_is_svm.pdf
Support vector machines TRENDS & CONTROVERSIESTRENDS & CONTROVERSIES By Marti A. Hearst University of California, Berkeley [email protected] My first exposure to Support Vector Machines came this spring when I heard Sue Dumais present impressive results on text categorization using this analysis technique.
https://en.wikipedia.org/wiki/Support-vector_machine
The soft-margin support vector machine described above is an example of an empirical risk minimization (ERM) algorithm for the hinge loss. Seen this way, support vector machines belong to a natural class of algorithms for statistical inference, and many of its unique features are due to the behavior of the hinge loss.
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