Introduction to SVM
Support Vector Machines, or SVM, play a crucial role in the world of artificial intelligence. This supervised learning model is widely used for classification and regression tasks. It effectively separates data points into different classes by finding the optimal hyperplane. The beauty of SVM lies in its ability to handle complex and high-dimensional data.
By leveraging the concept of support vectors, SVM ensures that the margin between different classes is maximized. This approach enhances the model’s predictive accuracy. SVM is also adaptable to various kernel functions, allowing it to operate efficiently in both linear and non-linear scenarios. As AI continues to evolve, the significance of SVM remains evident, making it a fundamental concept for anyone interested in machine learning.
