| 翻訳と辞書 |
| 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.
抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』
■ウィキペディアで「quadratic classifier」の詳細全文を読む
スポンサード リンク
| 翻訳と辞書 : 翻訳のためのインターネットリソース |
Copyright(C) kotoba.ne.jp 1997-2016. All Rights Reserved.
|
|