A Support Vector Machine
A supervised learning algorithm primarily used for classification tasks, but can also be adapted for regression through methods like Support Vector Regression (SVR). The algorithm is trained on a dataset of labeled examples, where each example is represented as a point in an n-dimensional feature space. The SVM algorithm finds an optimal hyperplane that separates classes in this space with the maximum margin possible.
Areas of application
- natural language processing
- image classification
- recommendation systems
- bioinformatics
- medical diagnosis
Example
For instance, an SVM algorithm can be used to classify images as either cats or dogs based on their features such as color, shape, and size. The algorithm would be trained on a dataset of labeled images, and then it could be used to predict the class label of new images not seen before.