Hey there, tech enthusiasts! Today, let’s deep dive into the fascinating world of Support Vector Machines (SVM). We’ll explore the concept, workings, and applications of SVMs in the realm of Machine Learning.
Support Vector Machines (SVMs) are incredibly versatile, offering a wide range of applications from spam email detection to image recognition. For instance, in facial recognition, SVMs can identify specific individuals based on facial features extracted from images. Moreover, in medical diagnostics, SVMs can detect anomalies like tumors in MRI scans.
The goal of the SVM algorithm is to create a decision boundary or line that effectively separates a given dataset into different classes. Once the decision boundary is established, new examples can be classified into the appropriate classes relatively easily. In the SVM algorithm, this decision boundary is known as a hyperplane. The challenge then becomes accurately drawing this hyperplane.