![]() ![]() ![]() Let’s assume we have red and black labels with the features denoted by x and y. The working of a support vector machine can be better understood through an example. See More: What Is a Neural Network? Definition, Working, Types, and Applications in 2022 How Does a Support Vector Machine Work? Since then, SVMs have gained enough popularity as they have continued to have wide-scale implications across several areas, including the protein sorting process, text categorization, facial recognition, autonomous cars, robotic systems, and so on. The idea behind the SVM algorithm was first captured in 1963 by Vladimir N. This technique is usually referred to as the kernel trick, wherein data transformation into higher dimensions is achieved efficiently and inexpensively. While introducing additional dimensions, the data is not entirely transformed as it can act as a computationally taxing process. The function simplifies the data boundaries for non-linear problems by adding higher dimensions to map complex data points. Kernel functions rely on the process of mapping complex datasets to higher dimensions in a manner that makes data point separation easier. In the mathematical context, an SVM refers to a set of ML algorithms that use kernel methods to transform data features by employing kernel functions. However, with the rise in computationally intensive multiclass problems, several binary classifiers are constructed and combined to formulate SVMs that can implement such multiclass classifications through binary means. SVMs are potentially designed for binary classification problems. Such hyperplanes are easier to define for linearly separable problems however, for real-life problems or scenarios, the SVM algorithm tries to maximize the margin between the support vectors, thereby giving rise to incorrect classifications for smaller sections of data points. ![]() SVMs Optimize Margin Between Support Vectors or ClassesĪs seen in the above figure, the margin refers to the maximum width of the slice that runs parallel to the hyperplane without any internal support vectors. The support vector representation is shown in the figure below: The hyperplane is localized in such a manner that the largest margin separates the classes under consideration. Technically, the primary objective of the SVM algorithm is to identify a hyperplane that distinguishably segregates the data points of different classes. SVMs are widely adopted across disciplines such as healthcare, natural language processing, signal processing applications, and speech & image recognition fields. How Does a Support Vector Machine Work?Ī support vector machine (SVM) is a machine learning algorithm that uses supervised learning models to solve complex classification, regression, and outlier detection problems by performing optimal data transformations that determine boundaries between data points based on predefined classes, labels, or outputs. ![]()
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