Support Vector Machine (SVM) is a powerful classification algorithm used for both regression and classification problems. It is a supervised learning algorithm that finds the best boundary between two classes. In this tutorial, we will explore the basics of SVM, its types, and its application.

What is SVM?

SVM is a machine learning algorithm that creates a model for classification and regression problems. The goal of SVM is to find a hyperplane in an N-dimensional space that distinctly classifies the data points.

Key Concepts of SVM

  • Hyperplane: A hyperplane is a decision boundary in an N-dimensional space.
  • Support Vectors: Data points that are closest to the hyperplane are called support vectors.
  • Maximal Margin: The maximum distance between the hyperplane and the nearest data points is called the maximal margin.

Types of SVM

  1. Linear SVM: It can be used for linearly separable data.
  2. Non-Linear SVM: It uses kernel functions to map the data into a higher-dimensional space where it becomes linearly separable.

How SVM Works

  1. The SVM algorithm finds the best hyperplane that maximizes the margin between the two classes.
  2. The distance between the hyperplane and the nearest data points is maximized.
  3. The support vectors are the data points that are closest to the hyperplane.

SVM Hyperplane

SVM Application

SVM is widely used in various applications, such as:

  • Image Recognition
  • Text Classification
  • Medical Diagnosis

For more detailed information on SVM applications, you can check out our Machine Learning Applications.

Conclusion

SVM is a powerful and versatile machine learning algorithm that is widely used in various fields. Understanding the basics of SVM will help you to apply it effectively to your data. For further reading, you can explore our SVM Resources.