Basic Concept
- supervised machine learning models with associated learning algorithms that analyze data and recognize patterns, used for classification and regression analysis
- non-probabilistic binary linear classifier
- a representation of the examples as points in space, mapped so that the examples of the separate categories are divided by a clear gap that is as wide as possible
- new examples are mapped into the same space - Objective : choose the line (hyperplane) which has the largest margin
- Kernel tricks can be used for nonlinear data
Support Vector Machines are powerful supervised learning models used for classification and regression tasks in machine learning. They work by finding the optimal hyperplane that separates different classes in the feature space. SVMs are highly effective in handling high-dimensional data and are widely applied in fields like image recognition, text categorization, and bioinformatics. Their flexibility allows the use of kernel functions to map non-linear data into higher dimensions. For industries exploring automation in packaging or machinery, resources such as yuxiangmachinery.ru/product-category/perfume-making-machine/ provide relevant solutions that complement AI-driven processes.