Construction of Binary Tree Classifier Using Linear SVM for Large-Scale Classification

机译：基于线性SVM的二叉树分类器的大规模分类构建

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Support vector machines (SVM) with kernel can solve nonlinear problem, but when the size of the problem is relatively large, the solving speed will be slow, which is not conducive to real-time applications. For linear SVM, it has fast computational speed, but its classification accuracy is usually not guaranteed. This paper proposes a binary tree classifier with linear SVM, which makes a tradeoff between computational speed and classification accuracy. If the local error rate is below a pre-set threshold, leaf nodes that make the final decision are generated; Otherwise, recursive construction of non-leaf nodes is performed. The final tree structure expresses the hierarchical division of given pattern classes. Experiments show that the proposed method ensures the generalization ability while responding rapidly.

机译：支持核的支持向量机（SVM）可以解决非线性问题，但是当问题规模较大时，求解速度会很慢，不利于实时应用。对于线性SVM，它具有快速的计算速度，但是通常不能保证其分类精度。本文提出了一种具有线性支持向量机的二叉树分类器，它在计算速度和分类精度之间进行了权衡。如果本地错误率低于预设阈值，则生成做出最终决策的叶节点;否则，将生成最终决策的叶节点。否则，将执行非叶节点的递归构造。最终的树形结构表示给定模式类别的层次划分。实验表明，该方法在快速响应的同时，保证了泛化能力。

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《International Conference on Robots Intelligent System》|2018年|471-474|共4页
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Qiangkui Leng; Shurui Wang; Dehai Shen;
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Conferences; Robots; Intelligent systems;

机译：会议;机器人;智能系统;

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机译：一种基于SVM二进制分类器的DAGSVM多分类算法来解决石板瓦分类问题
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机译：决策树SVM：用于非线性分类的线性SVM的扩展
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机译：使用线性SVM构建二叉树分类器进行大规模分类
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机译：使用SVM类型分类器的软决策分层分类。
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机译：与传统的分类树相比增强的分类树导致心血管结果分类的准确性略有改善
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机译：图7：具有加权准确性和F1的箱子图，用于理论与旋转仪和旋转仪系列的理论分类，使用最近的邻居（NN），决策树（DT），随机森林（RF），多层植物（MLP）和支持向量机（SVM）分类器。
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机译：使用二叉树分类器对所选雷达图像模式进行分类

Construction of Binary Tree Classifier Using Linear SVM for Large-Scale Classification

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