2025年1月27日 · Support Vector Machine (SVM) is a supervised machine learning algorithm that excels in classification tasks by finding the optimal hyperplane that maximizes the margin between different classes, utilizing support vectors and kernel functions for both linear and non-linear data.

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 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. Still effective in cases where number of dimensions is greater than the number of samples.

2023年2月2日 · Support 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.

2023年9月1日 · 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.

2024年10月7日 · A Support Vector Machine (SVM) is a supervised machine learning algorithm used for both classification and regression tasks. However, it’s primarily known for its prowess in classification problems.

2023年12月27日 · 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.

2025年3月10日 · What is a Support Vector Machine (SVM)? A Support Vector Machine (SVM) is a machine learning algorithm used for classification and regression. It finds the best line (or hyperplane) to separate data into groups, maximizing the distance between the closest points (support vectors) of each group.

2023年7月1日 · Support 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...

2020年7月1日 · Support vector machines are a set of supervised learning methods used for classification, regression, and outliers detection. All of these are common tasks in machine learning. You can use them to detect cancerous cells based on millions of images or you can use them to predict future driving routes with a well-fitted regression model.