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For the task of visual-based automatic product image classification for e-commerce, this paper constructs a set of support
vector machine (SVM) classifiers with different model representations. Each base SVM classifier is trained with either different
types of features or different spatial levels. The probability outputs of these SVM classifiers are concatenated into feature
vectors for training another SVM classifier with a Gaussian radial basis function (RBF) kernel. This scheme achieves state-of-the-art
average accuracy of 86.9% for product image classification on the public product dataset PI 100. 相似文献
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针对异步电机早期定子故障诊断,根据电机定子故障的特点,采用小波变换极大模分析法检测故障信号突变点的位置;利用小波包各个频带能量的变化完成能最特征提取,采用BP神经网络故障识别算法识别电机的各种运行状态来诊断电机早期故障.仿真实验结果表明,小波分析和神经网络算法的结合能有效定位并检测异步电机的早期故障. 相似文献
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