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- W2344992011 abstract "支持向量机器(SVM ) 在模式识别和回归的评价显示出大潜力。由于工业开发要求，例如当模特儿的发酵进程，在逐渐地大的样品集合上改进训练性能是一个重要问题。然而，解决一个大优化问题是计算地集中的并且记忆集中。在这份报纸， SVM 回归(SVR ) 的几何解释被导出，并且 -SVM 为 L1 标准和 L2 标准惩罚 SVR 被扩大。进一步，吉伯算法，一个著名几何算法，被修改解决 SVR 问题。理论分析显示训练几何算法的介绍 SVR 为 SVM 分类有象他们的相应算法的一样的集中和计算的几乎相同的费用。试验性的结果证明几何方法是比用二次的编程的常规方法更有效的并且要求少得多记忆。" @default.
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- W2344992011 date "2012-09-15" @default.
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- W2344992011 title "A Geometric Approach to Support Vector Regression and Its Application to Fermentation Process Fast Modeling" @default.
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