A Support Vector Machine (SVM) is a versatile supervised learning algorithm used in machine learning for tasks like classification and regression. It creates boundaries between different groups based on their features. SVM includes linear and non-linear models and applies to various fields such as spam email filtering, handwriting recognition, medical diagnosis, and stock market prediction.
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What is Support Vector Machine (SVM)?
A Support Vector Machine (SVM) is a powerful algorithm used in machine learning for tasks such as classification and regression. It excels in handling various tasks like spam email filtering, handwriting recognition, and image classification by drawing the best possible line or plane to separate different groups based on their features.
Types of SVM
Linear Support Vector Machine
Works best when data can be easily split into two groups using a straight line.
Non-Linear Support Vector Machine
Used when data cannot be classified into two separate groups with a straight line.
How Does It Work?
SVM finds the best line or plane to separate groups by maximizing the distance from the closest points to the line, known as the “margin.” It uses something called “kernel tricks” to handle non-linear separable data.
Use Cases And Applications
- Spam Email Filtering
- Handwriting Recognition
- Medical Diagnosis
- Image Classification
- Stock Market Prediction
For more information, you can refer to the following sources:
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