What Support Vector Machine (SVM) Meaning, Applications & Example
A supervised learning algorithm for classification and regression.
What is Support Vector Machine (SVM)?
Support Vector Machine (SVM) is a supervised machine learning algorithm commonly used for classification and regression tasks. It works by finding the hyperplane that best separates different classes in the feature space. SVM aims to maximize the margin between the closest data points of each class, known as support vectors, leading to better generalization.
How SVM Works
- Linear SVM: In simple cases, SVM finds a straight line (or hyperplane in higher dimensions) that separates data points of different classes with maximum margin.
- Non-linear SVM: When data is not linearly separable, SVM uses a kernel trick to map data into a higher-dimensional space where a linear hyperplane can be used for separation.
- Support Vectors: The closest data points to the hyperplane that are used to define the decision boundary.
Applications of SVM
- Image Classification: SVM is often used for classifying images, such as handwriting recognition or facial recognition .
- Text Classification: It is effective for spam detection, sentiment analysis , or categorizing documents into topics.
- Bioinformatics: SVM is used for protein classification, gene expression analysis, and other biological data analysis tasks.
Example of SVM
In spam email detection, SVM can be trained on a set of labeled emails (spam or not spam) with features like the frequency of certain words or the sender’s address. Once trained, the SVM model can classify new emails as spam or not based on the decision boundary it learned from the training data. The support vectors (important emails near the boundary) help define the best classification model.