Support Vector Machines (SVM)
Support Vector Machines are powerful tools for classification, regression, and outlier detection. This guide will help you understand how to implement and optimize SVMs efficiently. 🐸
Understanding the Basics
SVMs are supervised learning models with associated learning algorithms that analyze data used for classification and regression analysis. They are one of the most robust prediction methods, being widely used in fields such as finance, marketing, and bioinformatics.
Applications
- Image and speech recognition
- Genomic and bioinformatics applications
- Text and hypertext categorization
- Time series prediction