Support Vector Machines (SVMs) are a popular type of supervised learning algorithm used for classification and regression tasks. Here’s a brief overview:
What is an SVM?
An SVM is a machine learning model that aims to find a hyperplane that maximally separates classes in a dataset. It does this by finding the hyperplane that maximizes the margin between classes.
Key Concepts:
1. Hyperplane: A decision boundary that separates classes.
2. Margin: The distance between the hyperplane and the closest data points (called support vectors).
3. Support Vectors: Data points that lie closest to the hyperplane and define the margin.
How SVMs Work:
1. Training: The algorithm takes labeled data as input and finds the optimal hyperplane that separates classes.
2. Classification: New, unseen data is classified by determining which side of the hyperplane it falls on.
Types of SVMs:
1. Linear SVM: Used for linearly separable data.
2. Non-Linear SVM: Used for non-linearly separable data, using kernel functions (e.g., polynomial, radial basis function).
Kernel Functions:
1. Linear Kernel: No transformation, used for linearly separable data.
2. Polynomial Kernel: Transforms data into a higher-dimensional space.
3. Radial Basis Function (RBF) Kernel: Transforms data into a higher-dimensional space using a Gaussian distribution.
Advantages:
1. Robust to noise: SVMs are robust to noisy data.
2. High-dimensional data: SVMs can handle high-dimensional data effectively.
3. Non-linear classification: SVMs can handle non-linear classification tasks using kernel functions.
Common Applications:
1. Image classification: SVMs are used in image classification tasks, such as object detection.
2. Text classification: SVMs are used in text classification tasks, such as spam filtering.
3. Bioinformatics: SVMs are used in bioinformatics for protein classification and gene expression analysis.