What is a “support vector machine” (SVM) used for in AI?
a) Classification and regression tasks
b) Data clustering
c) Dimensionality reduction
d) Image generation
Answer:
a) Classification and regression tasks
Explanation:
Support Vector Machines (SVMs) are supervised learning models used for classification and regression tasks. They work by finding the optimal hyperplane that best separates the data points into different classes with the maximum margin.
SVMs are particularly effective in high-dimensional spaces and can be extended to non-linear classification tasks by using kernel methods. This makes SVMs a versatile tool for many real-world applications, including text classification, image recognition, and bioinformatics.
The main advantage of SVMs is their ability to handle both linear and non-linear problems effectively, making them a popular choice for many AI tasks involving classification.