A Support Vector Machine (SVM) classifier is a type of machine learning algorithm that is used for classification tasks. It is a supervised learning algorithm that is used to classify data into different categories based on the features of the data.

The SVM classifier works by finding the best hyperplane that separates the data into different classes. The hyperplane is chosen in such a way that it maximizes the margin between the two classes. The margin is the distance between the hyperplane and the closest data points from each class.

The SVM classifier can be used for both linear and non-linear classification tasks. In the case of non-linear classification tasks, the SVM classifier uses a technique called kernel trick to transform the data into a higher-dimensional space where it can be separated by a hyperplane.

The SVM classifier is widely used in various applications such as image classification, text classification, and bioinformatics. It is known for its high accuracy and robustness in handling complex datasets.