Máquina de vectores de soporte lineal (SVM) is a supervised aprendizaje automático algorithm primarily used for classification tasks. It works by finding the optimal hyperplane that separates data points of different classes in a espacio de características. This hyperplane is defined as a flat affine subspace of one dimension less than the input space, which means that for two-dimensional data, the hyperplane is a line; for three-dimensional data, it’s a plane.
The goal of a Linear SVM is to maximize the margin between the two classes, which is the distance between the hyperplane and the nearest data points from either class, known as the support vectors. By focusing on these support vectors, Linear SVM is robust to outliers and noise, leading to better generalization al clasificar nuevos datos.
Linear SVM is particularly effective when the data is linearly separable, meaning that classes can be separated by a straight line (or hyperplane). However, if the data is not linearly separable, techniques such as the kernel trick can be applied to transform the data into a higher-dimensional space where a linear separation is possible. In its simplest form, Linear SVM can be implemented using the técnicas de optimización de multiplicadores de Lagrange o mediante métodos de descenso de gradiente.
Linear SVM has numerous applications, including text classification, image recognition, and bioinformatics. Its efficiency and effectiveness make it a popular choice in various domains where clasificación binaria es necesario.