A Support Vector Machine (SVM) is a supervised machine learning algorithm widely used for classification and regression tasks. It operates by finding the optimal hyperplane that best separates data points of different classes in a high-dimensional space. SVMs are particularly effective in high-dimensional spaces and are still effective when the number of dimensions exceeds the number of samples. They also use a subset of training points in the decision function, called support vectors, making them memory efficient.
Key Features:
- Effective in High-Dimensional Spaces: SVMs perform well even when the number of dimensions is greater than the number of samples.
- Memory Efficiency: By utilizing support vectors, SVMs are memory efficient, focusing on the most critical data points for decision-making.
- Versatility: SVMs can be adapted for both linear and non-linear classification tasks through the use of different kernel functions.
Applications:
- Classification: SVMs are commonly used for image recognition, text classification, and bioinformatics applications.
- Regression: They are also applied in predicting continuous values, such as stock prices or temperature forecasts.
- Outlier Detection: SVMs can identify anomalies or outliers in data, which is useful in fraud detection and network security.