Support Vector Machines
What is Support Vector Machines?
Support Vector Machines (SVMs) are a type of algorithm in the field of artificial intelligence that are particularly useful for classification challenges. The core idea is to find the optimal hyperplane that best separates data points of different classes in a high-dimensional space. This hyperplane maximizes the margin between the closest points of the classes, known as support vectors. SVMs are effective in high-dimensional spaces and are versatile, as different kernel functions can be specified for the decision function. They are used in various practical applications such as image recognition, medical diagnosis, and stock market analysis. By focusing on the points that are most difficult to classify, SVMs ensure robustness and accuracy, making them a reliable choice for many AI tasks.
A supervised machine learning algorithm used for classification and regression tasks.
Examples
- Email Spam Detection: SVMs can be trained to differentiate between spam and non-spam emails by analyzing features such as the frequency of certain words, sender information, and other metadata.
- Handwritten Digit Recognition: In systems like postal automated reading, SVMs can classify handwritten digits by learning from datasets like MNIST, improving the accuracy of automated mail sorting.
Additional Information
- SVMs can be used for both linear and non-linear classification by using kernel tricks.
- They are less effective when the data set has more noise, i.e., target classes are overlapping.