Support Vector Machines (SVMs) are a popular machine learning model used for both classification and regression tasks. They are well-known for their effectiveness in high-dimensional spaces and flexibility with various kernels.
Understanding SVMs
SVMs work by finding the hyperplane that best separates the data into different classes. This is done by maximizing the margin between the closest points of the classes known as support vectors.
Applications
- Image classification
- Text categorization
- Bioinformatics
Benefits of Using SVMs
- Effective in high-dimensional spaces
- Versatile with different kernel functions
- Robust to overfitting, especially in high-dimensional space
For more information on SVMs, visit the SVM Guide.