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https://gerardnico.com/data_mining/support_vector_machine
A support vector machine is a Classification method. supervised algorithm used for: Classification and Regression (binary and multi- class problem) anomalie detection (one class problem)
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.futurelearn.com/courses/data-mining-with-weka/3/steps/290151
If the classes cannot be separated by a straight line, a device called the “kernel trick” enables support vector machines to make boundaries of different shapes, not just straight lines. Support vector machines are very resilient to overfitting, because the boundary depends on just a few well-chosen data points, not the entire training set.
https://hackerbits.com/data/support-vector-machine-svm-data-mining-algorithm/
Support vector machine (SVM) learns a hyperplane to classify data into 2 classes. At a high-level, SVM performs a similar task like C4.5 except SVM doesn’t use decision trees at all.
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://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.
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