Linear SVM Overview
SVMs have applications in many disciplines, including customer relationship management (CRM), facial and other image recognition, bioinformatics, text mining concept extraction, intrusion detection, protein structure prediction, and voice and speech recognition.
An SVM works by mapping data to a high-dimensional feature space so that data points can be categorized, even when the data are not otherwise linearly separable. A separator between the categories is found, then the data are transformed in such a way that the separator could be drawn as a hyperplane. Following this, characteristics of new data can be used to predict the group to which a new record should belong.
The mathematical function used for the transformation is known as the kernel function. The Linear SVM node uses a linear kernel. A linear kernel function is recommended when linear separation of the data is straightforward. In other cases, another function should be used (see the SVM node).
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