| 基于参数优化的支持向量机战场多目标声识别 |
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| 引用本文： | 李京华,张聪颖,倪宁.基于参数优化的支持向量机战场多目标声识别[J].探测与控制学报,2010,32(1). |
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| 作者姓名： | 李京华 张聪颖 倪宁 |
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| 作者单位： | 西北工业大学电子信息学院,陕西,西安,710072 |
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| 摘 要： | 针对网格搜索支持向量机(SVM)参数的方法存在复杂度高、运算量大等不足,提出了一种改进的网格搜索SVM分类器的最佳参数选择算法。将其应用于战场多目标SVM分类器中,对该分类器与KNN分类器和改进BP神经网络分类器进行的分类对比实验表明,改进的网格搜索SVM分类器参数选择算法可以有效地减少SVM分类器的运算量、改进学习性能并提高识别率。
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| 关 键 词： | 支持向量机 目标声识别 参数选择 网格搜索 分类器 |
Multi-target Acoustic Identification in Battlefield Based on SVM of Parameter Optimization |
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| LI Jinghua,ZHANG Congying,Ni Ning.Multi-target Acoustic Identification in Battlefield Based on SVM of Parameter Optimization[J].Journal of Detection & Control,2010,32(1). |
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| Authors: | LI Jinghua ZHANG Congying Ni Ning |
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| Affiliation: | LI Jinghua,ZHANG Congying,Ni Ning(Electronic Information College,Northwestern Polytechnical University,Xi\'an 710072,China) |
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| Abstract: | The original grid-search algorithm for choosing parameters of Support Vector Machine(SVM) has large amount of calculation in the training processes.An improved grid-search algorithm is proposed to choose the optimal parameters of SVM.The battlefield multi-target SVM classifier is designed using this algorithm.Target classification experiments are done using K-nearest neighborhood classifier,improved BP neural network classifier and SVM classifier respectively.The result shows that the improved grid-search a... |
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| Keywords: | support vector machine target acoustic identification parameter selection grid-search classifier |
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