Define 'support vector machine'.

A support vector machine (SVM) is indeed defined as a supervised learning model specifically designed for classification tasks. It operates by identifying the optimal hyperplane that best separates different classes within the data while maximizing the margin between these classes. This separation is crucial in order to effectively classify unseen data points.

In its operation, SVM works with a set of training data, where each example is labeled with one of the classes. The goal of SVM is to find a hyperplane that not only divides the classes but does so with the greatest distance from the nearest data points of any class, also known as support vectors. This approach helps to improve the model's generalization when making predictions on new, unseen data.

The focus of SVM on finding the best boundary to separate classes highlights its role within supervised learning frameworks, where labeled data is essential for training. This aspect contrasts with unsupervised methods, which do not rely on labeled data, or methods that specifically work with features or optimizations in other contexts.