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https://fr.mathworks.com/discovery/support-vector-machine.html
A support vector machine (SVM) is a supervised learning algorithm that can be used for binary classification or regression. Support vector machines are popular in applications such as natural language processing, speech and image recognition, and computer vision.. A support vector machine constructs an optimal hyperplane as a decision surface such that the margin of …
https://en.wikipedia.org/wiki/Support_vector_machine
Support-vector machine weights have also been used to interpret SVM models in the past. Posthoc interpretation of support-vector machine models in order to identify features used by the model to make predictions is a relatively new area of research with special significance in the biological sciences. History
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:
https://fr.mathworks.com/help/stats/support-vector-machine-classification.html
Support vector machines for binary or multiclass classification. Train Support Vector Machines Using Classification Learner App. Create and compare support vector machine (SVM) classifiers, and export trained models to make predictions for new data.fitcsvm: Train binary support vector machine (SVM) classifier
https://www.mathworks.com/help/stats/support-vector-machines-for-binary-classification.html
Support Vector Machines for Binary Classification Understanding Support Vector Machines. Separable Data. Nonseparable Data. Nonlinear Transformation with Kernels. Separable Data. You can use a support vector machine (SVM) when your data has exactly two classes. An SVM classifies data by finding the best hyperplane that separates all data points ...
http://support-vector-machines.org/
Kernel-based techniques (such as support vector machines, Bayes point machines, kernel principal component analysis, and Gaussian processes) represent a major development in machine learning algorithms. Support vector machines (SVM) are a group of supervised learning methods that can be applied to classification or regression.
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