Technology
Support Vector Machines Explained
Discover Support Vector Machines (SVM), a powerful supervised learning algorithm used for classification and regression tasks in AI and machine learning.
What is it?
A Support Vector Machine (SVM) is a popular supervised machine learning algorithm used for classification and regression. The core idea is to find an optimal hyperplane that best separates data points of different classes in a high-dimensional space. This hyperplane is chosen to maximize the margin—the distance between it and the nearest data points from each class. These crucial points are called 'support vectors,' and they alone define the decision boundary, making the model memory efficient.
Why is it trending?
SVMs remain a cornerstone of machine learning due to their high accuracy, especially in high-dimensional spaces. They are effective even when the number of dimensions is greater than the number of samples. Their versatility is a key strength; by using different 'kernel functions' (like linear, polynomial, and RBF), they can solve complex, non-linear problems by mapping data into higher-dimensional spaces. This adaptability makes them a go-to model for a wide range of classification challenges where a clear margin of separation is needed.
How does it affect people?
SVMs power numerous applications that impact daily life. They are used in facial recognition systems, spam email filters, and medical diagnostics to classify tumors. In finance, SVMs help with credit scoring and stock market prediction. They are also integral to handwriting recognition on devices and text categorization for organizing large document databases, making information more accessible and systems more intelligent.