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 …
Support Vector Machine (SVM) Algorithm - GeeksforGeeks
Oct 24, 2025 · The key idea behind the SVM algorithm is to find the hyperplane that best separates two classes by maximizing the margin between them. This margin is the distance …
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 …
1.4. Support Vector Machines — scikit-learn 1.7.2 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 …
Support Vector Machines (SVM): An Intuitive Explanation
Jul 1, 2023 · SVMs are designed to find the hyperplane that maximizes this margin, which is why they are sometimes referred to as maximum-margin classifiers. They are the data points that …
What Are Support Vector Machine (SVM) Algorithms? - Coursera
Mar 11, 2025 · What is an SVM? An SVM algorithm, or a support vector machine, is a machine learning algorithm you can use to separate data into binary categories. When you plot data on …
Support Vector Machine (SVM) Algorithm - Great Learning
Mar 18, 2025 · Support Vector Machine (SVM) is a supervised machine learning algorithm used for classification and regression tasks. It is widely applied in fields like image recognition, text …