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![[Figure 1]](tz5065fig1mag.jpg)
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Figure 1
An illustration of the general design of support vector machines (SVMs). SVMs are initially trained by input of examples of each class and their associated feature values. A trained SVM is then able to predict the identity of future objects based on their feature values (left). The underlying mechanism of classification involves finding the set of hyperplanes that best divide the space between examples in N dimensions, where N is the number of values in a feature. Here, this is depicted as lines dividing two-dimensional space (right). Other types of SVMs allow nonlinear functions to divide this space. |
![[Figure 1]](tz5065fig1.jpg)
 | STRUCTURAL BIOLOGY |
ISSN: 2059-7983
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