SVM (EN)

Support Vector Machine Architecture

Support Vector Machines (SVM) are supervised learning algorithms used for classification and regression. They find the optimal separating hyperplane between data points of different classes with maximum distance to the nearest data points. SVMs can also solve non-linear problems through kernel tricks by mapping data into higher-dimensional spaces. They are particularly effective in high-dimensional spaces and with datasets that have clear decision boundaries.

Architecture

flowchart TD     A[Data points] --> B[Classification]     B --> C{Linear separation?}     C -->|Yes| D[Find optimal hyperplane]     C -->|No| E[Kernel trick]     E --> F[Map to higher-dimensional space]     F --> D     D --> G[Maximize margin]     G --> H[Result: Classification model] 

In Context

• Typically used together with feature selection and data preprocessing
• Related to: Neural networks, Random Forest, Decision Trees
• Example use cases: Text classification, image recognition, bioinformatics