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https://www.saedsayad.com/support_vector_machine.htm
A Support Vector Machine (SVM) performs classification by finding the hyperplane that maximizes the margin between the two classes. The vectors (cases) that define the hyperplane are the support vectors.
https://blog.statsbot.co/support-vector-machines-tutorial-c1618e635e93
Aug 15, 2017 · Support Vector Machine (SVM) Tutorial Introduced a little more than 50 years ago, they have evolved over time and have also been adapted to various other problems like regression, outlier analysis, and ranking.Author: Abhishek Ghose
https://www.quora.com/How-does-a-SVM-choose-its-support-vectors
Mar 22, 2016 · svm which means support vector machine wants the plane margin as maximum and uses two planes as margin one is pi+and anotger one is pi- .
https://stats.stackexchange.com/questions/313660/what-are-the-support-vectors-in-a-support-vector-machine
In the case of linearly separable data, the support vectors are those data points that lie (exactly) on the borders of the margins. These are the only points that are necessary to compute the margin (through the bias term $b$). For C-SVMs, however, I always get confused as to what exactly the support vectors …
http://web.mit.edu/6.034/wwwbob/svm-notes-long-08.pdf
Support Vector Machine (SVM) Support vectors Maximize margin •SVMs maximize the margin (Winston terminology: the ‘street’) around the separating hyperplane. •The decision function is fully specified by a (usually very small) subset of training samples, the support vectors. •This becomes a Quadratic programming problem that is easy
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.Author: Rohith Gandhi
https://scikit-learn.org/stable/modules/svm.html
Support vector machines (SVMs) are a set of supervised learning methods used for classification, regression and outliers detection. For optimal performance, use C-ordered numpy.ndarray (dense) or scipy.sparse.csr_matrix (sparse) with dtype=float64. 1.4.1.
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