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https://towardsdatascience.com/support-vector-machine-introduction-to-machine-learning-algorithms-934a444fca47
Jun 07, 2018 · Support Vector Machine — Introduction to Machine Learning Algorithms To separate the two classes of data points, there are many possible hyperplanes that could be chosen. Our objective is to find a plane that has the maximum margin, i.e the maximum distance between data points of …Author: Rohith Gandhi
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.
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. In which sense is the hyperplane obtained optimal? Let’s consider the following simple problem:
http://cs229.stanford.edu/notes/cs229-notes3.pdf
Support Vector Machines This set of notes presents the Support Vector Machine (SVM) learning al-gorithm. SVMs are among the best (and many believe are indeed the best) “off-the-shelf” supervised learning algorithms. To tell the SVM story, we’ll need to first talk about margins and the idea of separating data with a large “gap.”
http://www.cs.columbia.edu/~kathy/cs4701/documents/jason_svm_tutorial.pdf
2 Support Vector Machines: history II Centralized website: www.kernel-machines.org. Several textbooks, e.g. ”An introduction to Support Vector Machines” by Cristianini and Shawe-Taylor is one. A large and diverse community work on them: from machine learning, optimization, statistics, neural networks, functional analysis, etc.
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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