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Support vector machine - Wikipedia
In 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.

Support Vector Machine (SVM) Algorithm - GeeksforGeeks
Support Vector Machines (SVMs) are powerful supervised learning models used for classification and regression tasks. A key factor behind their popularity is their ability to handle both linear and non-linear data effectively. In this article, we will explore visualizing SVMs using Python and popular libraries like scikit-learn and Matplotlib. Suppo

What Is Support Vector Machine? - IBM
A support vector machine (SVM) is a supervised machine learning algorithm that classifies data by finding an optimal line or hyperplane that maximizes the distance between each class in an N-dimensional space.

1.4. Support Vector Machines — scikit-learn 1.5.2 documentation
Support vector machines (SVMs) are a set of supervised learning methods used for classification, regression and outliers detection. The advantages of support vector machines are: Effective in high dimensional spaces.

Support Vector Machine Explained. Theory, Implementation, and ...
Support Vector Machine (SVM) is probably one of the most popular ML algorithms used by data scientists. SVM is powerful, easy to explain, and generalizes well in many cases. In this article, I’ll explain the rationales behind SVM and show the implementation in Python.

Support Vector Machine — Introduction to Machine Learning ...
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.

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.