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Quadratic classifier : ウィキペディア英語版 | Quadratic classifier
A quadratic classifier is used in machine learning and statistical classification to separate measurements of two or more classes of objects or events by a quadric surface. It is a more general version of the linear classifier. ==The classification problem== Statistical classification considers a set of vectors of observations x of an object or event, each of which has a known type ''y''. This set is referred to as the training set. The problem is then to determine for a given new observation vector, what the best class should be. For a quadratic classifier, the correct solution is assumed to be quadratic in the measurements, so ''y'' will be decided based on : In the special case where each observation consists of two measurements, this means that the surfaces separating the classes will be conic sections (''i.e.'' either a line, a circle or ellipse, a parabola or a hyperbola). In this sense we can state that a quadratic model is a generalization of the linear model, and its use is justified by the desire to extend the classifier's ability to represent more complex separating surfaces.
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