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SVM神经网络的数据分类预测-葡萄酒种类识别-SVM neural network data classification forecast- wine species identification
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提出了一种支持矢量机的汉语声调识别新方法。论文首先在基频和对数能量的基础上,建立了一个适合于支
持矢量机分类的等维声调特征。然后对支持矢量机的多分类策略和不同核函数对声调识别的影响进行了实验研究。
与BP神经网络相比,支持矢量机具有更高的识别率和更强的推广能力。-This paper presents a novel support vector machine based Chinese tone recognition method.A
new tone recognition
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结合KNN(最近邻)算法和SVM(支持向量机)算法的分类器,,原创-KNN+SVM classification recognition algorithms, absolute originality
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SVM神经网络的数据分类预测----意大利葡萄酒种类识别-SVM neural network data classification forecast---- Italian wine species identification
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斯坦福大学的姚邦鹏开发的一个图像分类算法,使用random forest实现了图像的精细区域描述,赢得了PASCAL VOC2011图像分类竞赛中的winner prize.经测试,程序完全可运行,且提供了MAC和Windows下的两种程序。-Image Classification: An Integration of Randomization and Discrimination
in A Dense Feature Representation
The goal of our m
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模糊C均值脑电分类并使用了支持向量机对比,其中支持向量机使用了三种方法参数寻优。-fuzzy C means clustering for EEG classification,and use the SVM for campare. The SVM applied three methods to find the optimal apartments.
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一种基于小波支持向量机和粒子群算法的多故障诊断技术。-Multi-fault classification based on wavelet SVM with PSO algorithm
to analyze vibration signals from rolling element bearings
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svm分类的源代码例子,很基础的,适合初学者,一看就懂-svm the classification source code examples, very basic, for beginners to understand at a glance
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SVM的多分类问题MATLAB工具包。传统的SVM只支持二分类问题,MATLAB自带的工具包也存在同样的问题。可以在setpath中导入此包,便可轻松将SVM问题扩展到多分类的层面。-A matlab toolkit to solving multi-classification problems based on svm. Traditional svm and the matlab toolkit svmtrain can only support for bi-classificati
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二分类问题采用包括逻辑回归、最小二乘法、感知器算法(按下space不断迭代)、svm线性分类,另外还有高斯分线性分类(待完善),针对平面上两类点进行分类-Second classification using logistic regression, the method of least squares, perception algorithm (Press space iteratively) svm linear classification, in addition to the Ga
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一个advancedclassify类包含数据分类相关的类,是集体智慧编程一书中核方法与svm那章的代码-A advancedclassify contains data classification class, book nuclear collective wisdom of programming methods and svm chapter code
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提出了一种基于DCT提取人脸特征技术和支持向量机分类模型的人脸识别方法。利用离
散余弦变换可提取人脸可识别的大部分信息,而支持向量机作为分类器,在处理小样本、高维数等
方面具有独特的优势,且泛化能力很强,无需先验知识。从ORL 人脸库上的实验结果可以看出,
DCT特征提取是很有效的,且SVM的分类性能优于最近邻分类器,同时提高了整个系统的运算速
度。-A face recognition method based on DCT for face feature extractio
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这是修改之后的系统,主要用支持向量机SVM和水平方向梯度HOG特征进行特征目标识别分类-This is a system modifications, mainly with support vector machines SVM and horizontal direction gradient the HOG feature characteristic target recognition classification
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Heart beat time series calculation III E Research document
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基于图像检索的matlab代码,基于内容的图像检索中的一些关键环节:特征提取:颜色直方图;纹理特征等 相似度:马氏距离,欧氏距离等 相关反馈:机器学习方法,如SVM,神经网络等 检索与分类:两个很相似的样本距离很小,虽然两个不相似的样本距离未必很大-content-based image retrieval of some of the key issues : Feature Extraction : color histogram Texture characteristics of si
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是一种支持向量机的改进,能进行多类分类,解决了原SVM二分类的缺陷-Is a kind of support vector machine (SVM) improvement, can multiclass classification, solve the two original SVM classification of defects
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SVM EEG signal classification
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SVM is one of the classification method that is very applicable in management.
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lib svm is support vector machine which can be used in classification and regression test. thanks for open source
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SVM神经网络的数据分类预测,运行成功无错误,希望对大家有帮助-SVM neural network prediction data classification, run successfully without error, we want to help
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