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MULTI-RESOLUTION LEAST SQUARES SUPPORT VECTOR MACHINES

机译:多分辨率最小二乘支持向量机

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摘要

The Least Squares Support Vector Machines (LS-SVM) is an improvement to the SVM.Combined the LS-SVM with the Multi-Resolution Analysis (MRA), this letter proposes the Multi-resolution LS-SVM (MLS-SVM). The proposed algorithm has the same theoretical framework as MRA but with better approximation ability. At a fixed scale MLS-SVM is a classical LS-SVM, but MLS-SVM can gradually approximate the target function at different scales. In experiments, the MLS-SVM is used for nonlinear system identification, and achieves better identification accuracy.
机译:最小二乘支持向量机(LS-SVM)是对SVM的改进。将LS-SVM与多分辨率分析(MRA)相结合,提出了多分辨率LS-SVM(MLS-SVM)。该算法与MRA具有相同的理论框架,但具有更好的逼近能力。在固定比例下,MLS-SVM是经典的LS-SVM,但是MLS-SVM可以在不同比例下逐渐逼近目标函数。在实验中,MLS-SVM用于非线性系统辨识,具有较好的辨识精度。

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