Support Vector Machines (SVM) are a type of supervised learning algorithm used for classification and regression in Machine Learning problems.
The idea behind SVMs is to find a hyperplane that optimally separates the different classes of data. In the case of binary classification, the hyperplane divides the space into two regions, one for each class. In the case of regression, a hyperplane is sought that best fits the data.
To find the optimal hyperplane, SVMs seek to maximize the distance between the closest points of each class (called support vectors), known as the maximum margin. In case the data are not linearly separable, kernel techniques are used to transform the feature space into one of higher dimensionality where they can be separable.
SVMs are widely used in data classification in areas such as biology, finance and marketing, as well as in fraud detection, image recognition and natural language processing.
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