Support Vector Machines (SVMs) stand as a form of supervised machine learning technique utilized in tasks involving classification and regression. Their purpose revolves around identifying the most advantageous hyperplane, one that effectively distinguishes between distinct sets of data points, all the while maximizing the gap between these sets. This gap signifies the space between the hyperplane and the closest data points from each respective class, thereby empowering SVMs to attain heightened generalization capabilities when confronted with new, unseen data instances.
What sets SVMs apart is their ability to handle complex decision boundaries and high-dimensional spaces. They can be extended to non-linear classification through the use of kernel functions, which transform the data into higher dimensions, making it separable even when a linear boundary isn’t effective. SVMs aim to strike a balance between fitting the data well and avoiding overfitting, making them robust and versatile for various applications. SVMs have demonstrated success in fields such as image recognition, text analysis, and bioinformatics.
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