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Bookmark or Share- Support Vector Machine (SVM) Algorithm - GeeksforGeeksA Support Vector Machine (SVM) is a supervised machine learning algorithm used for both classification and regression tasks. While it can be applied to regression problems, SVM is best suited for classification tasks.
- Introduction to Support Vector Machines (SVM) - GeeksforGeeksSupport Vector Machines (SVMs) are a type of supervised learning algorithm that can be used for classification or regression tasks. The main idea behind SVMs is to find a hyperplane that maximally separates the different classes in the training data.
- An Idiot’s guide to Support vector machines (SVMs) - MITIn general, lots of possible solutions for a,b,c (an infinite number!) SVMs maximize the margin (Winston terminology: the ‘street’) around the separating hyperplane. The decision function is fully specified by a (usually very small) subset of training samples, the support vectors.
- Support Vector Machines (SVM): An Intuitive ExplanationSupport Vector Machines (SVMs) are a type of supervised machine learning algorithm used for classification and regression tasks. They are widely used in various fields, including pattern...
- Support vector machine - WikipediaIn machine learning, support vector machines (SVMs, also support vector networks [1]) are supervised max-margin models with associated learning algorithms that analyze data for classification and regression analysis.
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