搜索资源列表
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用matlab实现非线性支持向量机分类器对多类进行分类。-Using matlab to achieve non-linear support vector machine classifier for multi-class classification.
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用matlab实现了SVM算法，并且对两类点进行了分类，给出了图形化的结果。-SVM algorithm is implemented using matlab, and two points on the classification, given the graphical result.
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在PYTHON里面，采用LIBSVM，实现对TE数据的多类故障的分类。-In PYTHON inside, using LIBSVM, TE data to realize the classification of many types of failures.
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用支持向量机(SVM)实现分类与函数回归的源代码。内有实例，依湖芦画瓢即可。-Using support vector machine (SVM) classification and function to achieve the reunification of the source code. There are examples, according to Lake Lo paintings can ladle.
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用MATLAB编写的svm源程序，可以实现支持向量机，用于特征分类或提取-SVM prepared using MATLAB source code, you can achieve the support vector machine for feature classification or extraction
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用MATLAB编写的分类回归程序，通用。并给出了实例对分类效果进行检验。-MATLAB prepared using classification and regression procedures, General. And give examples of the classification results from such a test.
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利用支持向量机的分类特性，结合modis的云图像，对云相态进行分类，利用svm云相态分类算法源代码-The use of support vector machine classification of features, combined with clouds modis images of the cloud phase state classification using svm-phase state of cloud classification algorithm source c
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转发一个可视化的非线性支持向量机多分类源码，比较实用易学，值得进一步深入开发。-non-linear SVM multi-classification
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基于Gauss 核函数SVM分类机，使用二阶几何方法训练。-Gauss kernel function SVM classification based on machine, using the geometric method of second-order training.
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关于rbf神经网络实现图像分类的IEEE英文文献 和大家一起分享-The Research of Vehicle Classification Using SVM and KNN in a ramp
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在分类算法研究onThe应用运行长度的车型分类研究中使用坡道SVM和K近邻-The Research of Vehicle Classification Using SVM and KNN in a ramp
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A method for classification of image using svm kernel
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The sample application is able to perform both Classification and Regression using Support Vector Machines. It can read Excel spreadsheets and determines the task to be performed depending on the number of the columns in the sheet. If the input table
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HSSVM是一个用超球SVM（Hyper-Sphere Support Vector Machines）模型求解多分类问题的工具包，采用Java语言实现。开发该程序的主要目的，是利用超球SVM求解模型代替传统上借助于解二分类问题的经典SVM模型来求解多分类问题。本文将论述该程序的主要实现细节，包括相关算法及设计原理的描述。-HSSVM is an ultra ball SVM (Hyper-Sphere Support Vector Machines) to solve multi-classi
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本程序包括：论文SVM 用于基于块划分特征提取的图像分类，和相应的matlab实现其中图像划分以及特征提取、聚类均利用matlab6.5完成。
-The procedures include: paper by SVM for feature extraction based on block classification, and the corresponding realization of one image into matlab, and feature extraction,
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svm分类器，利用svm分类器分离的例子，数据来自UCI-svm classification using svm classifier separation example, data from UCI
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于多类模式分类，使用svm工具箱：LS_SVMlab
使用平台：Matlab6.5
-Multi-class pattern classification, using svm toolbox: LS_SVMlab using the platform: Matlab6.5
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the code is for classification using svm for remote sensing image , please see enjoy
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采用svm来做分类，一般能得到较满意的结果，但用svm做分类预测时需要调节相关的参数才能得到比较理想的预测分类准确率，那么svm的参数该如何选取？该程序主要说明如何更好地提升分类器性能。(Use svm to do the classification, the general can get more satisfactory results, but when using svm to do classification prediction need to adjust the relev
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算法功能是SVM分类，使用PCA降维处理，一个文件是直接分类，另一个是降维后分类(Classification using SVM algorithm)
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