Artificial Intelligence & Machine Learning
Support Vector Machine (SVM)
Definition
A Support Vector Machine (SVM) is a supervised learning algorithm used for classification and regression analysis. It works by finding the "hyperplane" that best separates the data points of different classes in a high-dimensional space.
Why It Matters
SVMs are powerful and versatile models that can be very effective, especially in high-dimensional spaces. The "kernel trick" allows them to find complex, non-linear boundaries between classes.
Contextual Example
To classify spam emails, an SVM would find the optimal dividing line (hyperplane) that separates the "spam" data points from the "not spam" data points in a space defined by features like the words used in the emails.
Common Misunderstandings
- The "support vectors" are the data points that lie closest to the decision boundary; they are the most critical points for defining the boundary.
- While very popular before the rise of deep learning, they are still a powerful tool.