SVM relies on supervised learning algorithms to perform classifications. It is a powerful method to classify unstructured data, make reliable predictions, and reduce redundant information.
What’s more, SVM has applications in different areas of daily life, such as:
- Face Detection Using image training data, SVM classifies pixels in images like a face or non-face
- Text ClassificationTraining data is used to categorize different types of documents. For instance, news articles can be classified as “business” or “entertainment.”
- Classifying ImagesBy classifying images with improved techniques, SVM increases search accuracy.
- BioinformaticsSVM algorithms have increased the effectiveness in protein homology detection, cancer classification, gene classification, and more.