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https://towardsdatascience.com/support-vector-machine-introduction-to-machine-learning-algorithms-934a444fca47
Jun 07, 2018 · Support Vector Machine, abbreviated as SVM can be used for both regression and classification tasks. But, it is widely used in classification objectives. What is Support Vector Machine? The objective of the support vector machine algorithm is to find a hyperplane in an N-dimensional space(N — the number of features) that distinctly classifies the data points.Author: Rohith Gandhi
https://docs.opencv.org/2.4/doc/tutorials/ml/introduction_to_svm/introduction_to_svm.html
A Support Vector Machine (SVM) is a discriminative classifier formally defined by a separating hyperplane. In other words, given labeled training data (supervised learning), the algorithm outputs an optimal hyperplane which categorizes new examples.
http://www.cs.columbia.edu/~kathy/cs4701/documents/jason_svm_tutorial.pdf
17 Linear Support Vector Machines II That function before was a little difficult to minimize because of the step function in ‘(y;y^) (either 1 or 0). Let’s assume we can separate the data perfectly. Then we can optimize the following: Minimize jjwjj2, subject to: (w xi +b) 1; if yi =1 (w xi +b) −1; if yi = −1 The last two constraints can be compacted to:
https://machinelearningmastery.com/support-vector-machines-for-machine-learning/
Support Vector Machines are perhaps one of the most popular and talked about machine learning algorithms. They were extremely popular around the time they were developed in the 1990s and continue to be the go-to method for a high-performing algorithm with little tuning.
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