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https://gerardnico.com/data_mining/support_vector_machine
Support vector machines are fantastic because they're very resilient to overfitting. Support vector machines are naturally resistant to overfitting because any interior points aren't going to affect the boundary.. There's just a few of the points (2, 3, ..) in each cloud that define the position of the line: the support vectors. All others instances in the training data could be deleted ...
https://hackerbits.com/data/support-vector-machine-svm-data-mining-algorithm/
These balls or data points are called support vectors, because they support the hyperplane. Is this supervised or unsupervised? This is a supervised learning, since a dataset is used to first teach the SVM about the classes.
https://docs.oracle.com/cd/B28359_01/datamine.111/b28129/algo_svm.htm
This chapter describes Support Vector Machines, a powerful algorithm based on statistical learning theory. Support Vector Machines is implemented by Oracle Data Mining for classification, regression, and anomaly detection.
https://www.togaware.com/datamining/survivor/Support_Vector.html
A Support Vector Machine (SVM) searches for so called support vectors which are data points that are found to lie at the edge of an area in space which is a boundary from one class of points to another. In the terminology of SVM we talk about the space between regions containing data points in different classes as being the margin between those classes.
https://www.cc.gatech.edu/classes/AY2008/cs7641_spring/handouts/yor12-introsvm.pdf
The support vector machine (SVM) 6,7 9 10 is a training algorithm for learning classification and regression rules from data, for example the SVM can be used to learn polynomial, radial basis function (RBF) and multi-layer perceptron (MLP) classifiers 7.
https://www-users.cs.umn.edu/~kumar001/dmbook/slides/chap4_svm.pdf
02/03/2018 Introduction’ toDataMining’ 1 Data$Mining Support’Vector’Machines Introduction’to’Data’Mining,’2nd Edition by Tan,’Steinbach,’Karpatne ...
https://t4tutorials.com/support-vector-machine-regression-svr/
Support vectors are the data points located nearly to the boundary. The distance of the points is minimum.
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.
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