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翻訳と辞書　辞書検索 [ 開発暫定版 ] スポンサード リンク Least squares support vector machine ： ウィキペディア英語版 Least squares support vector machine Least squares support vector machines (LS-SVM) are least squares versions of support vector machines (SVM), which are a set of related supervised learning methods that analyze data and recognize patterns, and which are used for classification and regression analysis. In this version one finds the solution by solving a set of linear equations instead of a convex quadratic programming (QP) problem for classical SVMs. Least squares SVM classifiers, were proposed by Suykens and Vandewalle.〔Suykens, J.A.K.; Vandewalle, J. (1999) "Least squares support vector machine classifiers", ''Neural Processing Letters'', 9 (3), 293-300.〕 LS-SVMs are a class of kernel-based learning methods. ==From support vector machine to least squares support vector machine== Given a training set $\backslash \; \_^N$ with input data $x\_i\; \backslash in\; \backslash mathbb^n$ and corresponding binary class labels $y\_i\; \backslash in\; \backslash$, the SVM〔Vapnik, V. The nature of statistical learning theory. Springer-Verlag, New York, 1995〕 classifier, according to Vapnik’s original formulation, satisfies the following conditions: : $$ \begin w^T \phi (x_i ) + b \ge 1, & \text \quad y_i = + 1 , \\ w^T \phi (x_i ) + b \le - 1, & \text \quad y_i = - 1 . \end Which is equivalent to : $y\_i\; \backslash left(\backslash right)\; \backslash ge\; 1,\backslash quad\; i\; =\; 1,\; \backslash ldots\; ,N\; \backslash ,\; ,$ where $\backslash phi(x)$ is the nonlinear map from original space to the high (and possibly infinite) dimensional space. 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「Least squares support vector machine」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.