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为提高含容参元件模拟电路软故障的诊断率,并考虑到单分类器分类精度的提升已达到了一个瓶颈,提出一种优化AdaBoost-SVM算法并将其应用于模拟电路故障诊断中。以OrCAD/PSpice软件中对电路进行Monte-Carlo分析的数据为基础,选取特征时,采用对时频信号中易直接测量的物理量归一化后组合的方式。实验结果表明,通过选取的组合特征向量,利用优化的AdaBoost-SVM算法,构造出具有差异度的SVM分类器并集成后,能够自适应地提升单SVM分类器性能,表现出更好的分类精度与泛化性能,能较好地满足容差模拟电路软故障诊断要求。 相似文献
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Existing water hazard detection methods usually fail when the features of water surfaces are greatly changed by the surroundings, e.g., by a change in illumination. This paper proposes a novel algorithm to robustly detect different kinds of water hazards for autonomous navigation. Our algorithm combines traditional machine learning and image segmentation and uses only digital cameras, which are usually affordable, as the visual sensors. Active learning is used for automatically dealing with prob- lems caused by the selection, labeling and classification of large numbers of training sets. Mean-shift based image segmentation is used to refine the final classification. Our experimental results show that our new algorithm can accurately detect not only 'common' water hazards, which usually have the features of both high brightness and low texture, but also 'special' water hazards that may have lots of ripples or low brightness. 相似文献
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ZHU Kai-hua QI Fei-hu JIANG Ren-jie XU Li 《浙江大学学报(A卷英文版)》2007,8(1):63-71
INTRODUCTION Text detection and segmentation from a naturalscene is very useful in many applications. With theincreasing availability of high performance, lowpriced, portable digital imaging devices, the applica-tion of scene text recognition is rapidly expanding. Byusing cameras attached to cellular phones, PDAs, orstandalone digital cameras, we can easily capture thetext occurrences around us, such as street signs, ad-vertisements, traffic warnings or restaurant menus.Automatic recogn… 相似文献
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近年来基于Adaboost的人脸检测算法因其快速和可接受的检测率得到了成功的应用,但Viola-Jones学习算法需要对级联分类器的每一个特征反复训练弱分类器显得非常缓慢。本文给出了一种新的级联检测器节点分类设计方法,首先将每个节点所有弱分类器的训练移到循环外,然后选择使强分类器有最小错误率的特征集代替选择单个最小加权误差的特征生成强分类器。实践表明该训练速度快于Viola-Jones的方法。 相似文献
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提出一种PCA和AdaBoost相结合的人脸识别方法。AdaBoost是一种提高任意给定学习算法准确度的方法,它有较高的正确率,不需要先验知识,只需要选择合适的迭代次数等。基于AdaBoost学习算法简单高效的特点,本文将PCA和AdaBoost方法相结合,形成一个强的分类器,提高人脸识别的准确率,有较好的可行性和实际意义。 相似文献
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