By incorporating PLAME into the linear. SVM solver (with slight modification), we show that this approach can fulfill all conditions in Table I for training SVM.
We develop the new piecewise-linear approximate measure (PLAME) for training additive kernel SVM models.
Abstract—Additive Kernel SVM has been extensively used in many applications, including human activity detection and pedestrian detection.
By incorporating PLAME with the well-known dual coordinate descent method, we theoretically show that this approach can achieve the above three conditions.
Jul 26, 2024 · 摘要: 研究了无线传感器网络中的运输车辆的检测问题, 并利用AR - Burg最大熵算法对不同车辆时间序列进行谱特征提取. 分类结果是使用支持向量分类器类得到 ...
Additive Kernel SVM has been extensively used in many applications, including human activity detection and pedestrian detection. Since training an additive ...
Mar 11, 2024 · Abstract. Additive Kernel SVM has been extensively used in many applications, including human activity detection and pedestrian detection.
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Existing SVM training methods (those references in the above table can be found in our TKDE paper.) cannot simultaneously fulfill these three conditions.
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Random Binary Mappings for Kernel Learning and Efficient SVM · Fast and accurate image classification with histogram based features and additive kernel SVM.
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Jun 11, 2022 · Chan TLi ZU LCheng R(2024)PLAME: Piecewise-Linear Approximate Measure for Additive Kernel SVM (Extended abstract)2024 IEEE 40th ...