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Description: 一个包含丰富内容的流形学习算法工具包，有图形示例文件demo.fig，包括laplacian特征映射算法、流形规则调整、svm分类算法等内容，希望对研究机器学习的朋友有用。-contains a rich content of the manifold learning algorithm tool kit, graphics documents demo.fig example, laplacian features include mapping algorithm manifold rules adjustments svm classification algorithm, want to study machine learning useful friends.
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Size: 158447 |
Author: 江泉 |
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Description: 一个包含丰富内容的流形学习算法工具包，有图形示例文件demo.fig，包括laplacian特征映射算法、流形规则调整、svm分类算法等内容，希望对研究机器学习的朋友有用。-contains a rich content of the manifold learning algorithm tool kit, graphics documents demo.fig example, laplacian features include mapping algorithm manifold rules adjustments svm classification algorithm, want to study machine learning useful friends.
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Size: 14053 |
Author: liyanxmu |
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Description: 现在比较流行的SVM算法的一个DEMO，用于处理分类问题，欢迎大家试用。-Now popular SVM algorithm is a DEMO, used to deal with classification problems, welcomed the U.S. trial.
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Size: 168960 |
Author: lvlu |
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Description: 这是表达支持向量机一个demo，可以帮助更好的理解svm-This is an expression of support vector machine demo, can help a better understanding of svm
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Size: 318464 |
Author: 成友中 |
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Description: 支持向量机工具箱,内含demo程序,教你如何训练SVM和进行特征分类-Suppot Vector Machine (SVM) toolbox, Author Dr Gavin C. Cawley
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Size: 461824 |
Author: 王平 |
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Description: 解释svm原理用的demo源码並且利用簡單的繪圖秀出結果-Svm theory to explain the demo with source code and showed the results of a simple drawing
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Size: 3072 |
Author: Steve Evan |
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Description: 用matlab编写的用于线性二分类的SVM支撑向量机实例-the demo of svm two classification written in matlab
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Size: 23552 |
Author: 王龙 |
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Description: 基于梯度方向直方图( H OG) 特征的行人检测是目前检测精度较高的主流方法。针对基于梯度直方图特征的
行人检测存在检测精度还有待提高、向量维数大的问题, 提出使用梯度直方图统计特征加颜色频率和肤色特征描述行
人, 选取一些分类能力较强的block 作为最后的特征, 使用线性SVM 分类。在INRIA 库上的实验证明, 该方法能有效地
提高检测精度。-H istog r am o f or iented g radient( H OG) based on pedestr ian detect ion is the popular method w ith t he hig hest
detection rat e curr ent ly. To further im pr ove its detection r ate and decrease its larg e dimensions o f feat ur es, this paper in
tegr ates HOG w ith color frequent and skin colo r feature to descr ibe pedestr ian, w e select some blo ck features w ith better
classify ing ability as t he final featur e, then use linear SVM as our classifier. T he ex per iment o n INRIA demo nstr ates t he
hig h detection r ate of our met ho d.
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Size: 17686528 |
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Description: LS-SVM Leave-One-Out Cross-Validation Demo
G. C. Cawley, "Leave-one-out cross-validation based model selection criteria for weighted LS-SVMs", Proceedings of the International Joint Conference on Neural Networks (IJCNN-2006), pages 1661-1668, Vancouver, BC, Canada, July 16-21 2006. (pdf)theoval.cmp.uea.ac.uk/~gcc/matlab/
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Size: 5120 |
Author: leozajung |
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Description: 一个用BoW|Pyramid BoW+SVM进行图像分类的Matlab Demo
-Image Classification using Bag of Words and Spatial Pyramid BoW
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Size: 3573760 |
Author: 赵宇 |
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Description: 这个就是SVM识别的，说是世界识别冠军程序。源程序不是在windows 下的，这个该成了Windows下的，打开DEMO.M文件直接运行。检测的程序改好了，-This is the SVM identified, said that the world championship recognition program. The source is not in the windows, which became the Windows, open the file directly DEMO.M run. Detection procedures changing for the better,
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Size: 7487488 |
Author: gy |
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Description: This folder contains the following sub-folders which are essential in our project:
1.Raw Data
All the raw data collected from Flagstaff hill, CMU Athletic Field, and Railroad on Neville St.
2.Filter
Filter to rule out signal of Channel 1 and Channel 6
3.SVM_quadratic kernel_39
SVM code with quadratic kernel
4.PCA+KNN_62 :
PCA+KNN code
5.PCA+SVM_polynomial Kernel_50
PCA+SVM code with polynomial kernel
6.ANN
Artificial Neural Network code
7.PlotTerrain
Project demo video with actual result -This folder contains the following sub-folders which are essential in our project:
1.Raw Data
All the raw data collected from Flagstaff hill, CMU Athletic Field, and Railroad on Neville St.
2.Filter
Filter to rule out signal of Channel 1 and Channel 6
3.SVM_quadratic kernel_39
SVM code with quadratic kernel
4.PCA+KNN_62 :
PCA+KNN code
5.PCA+SVM_polynomial Kernel_50
PCA+SVM code with polynomial kernel
6.ANN
Artificial Neural Network code
7.PlotTerrain
Project demo video with actual result
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Size: 3803136 |
Author: Chao |
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Description: This demo explains the use of the advanced object oriented interface model. The model is the generic object which collects all relevant signals, parameters, options and functions related to an application of the LS-SVM. This interface is depreciated for the casional users because of the implicit nature: the distinction between in- and output vanishes. If one wants to use the full power of LS-SVMlab, one is recommended to go through this demo. The options can be divided in 5 classes, the general LS-SVM options the trainpoint administration, the options of the Conjugate Gradient algorithm the preprocess options and the encoding options. The demo shows how to control the preprocessing and the coding using the appropriate model. It is adviced to check carefully the model s options before starting the calculations.-This demo explains the use of the advanced object oriented interface model. The model is the generic object which collects all relevant signals, parameters, options and functions related to an application of the LS-SVM. This interface is depreciated for the casional users because of the implicit nature: the distinction between in- and output vanishes. If one wants to use the full power of LS-SVMlab, one is recommended to go through this demo. The options can be divided in 5 classes, the general LS-SVM options the trainpoint administration, the options of the Conjugate Gradient algorithm the preprocess options and the encoding options. The demo shows how to control the preprocessing and the coding using the appropriate model. It is adviced to check carefully the model s options before starting the calculations.
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Size: 12288 |
Author: hifa |
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Description: This a simple demo, solving a simple regression task using LS-SVMlab. A dataset is constructed in the right formatting. The data are represented as matrices where each row contains one datapoint. In order to make an LS-SVM model, we need 2 extra parameters: gamma (gam) is the regularization parameter, determining the trade-off between the fitting error minimization and smoothness of the estimated function. sigma^2 (sig2) is the kernel function parameter of the RBF kernel. The parameters and the variables relevant for the LS-SVM are passed as one cell. This cell allows for consistent default handling of LS-SVM parameters and syntactical grouping of related arguments. -This is a simple demo, solving a simple regression task using LS-SVMlab. A dataset is constructed in the right formatting. The data are represented as matrices where each row contains one datapoint. In order to make an LS-SVM model, we need 2 extra parameters: gamma (gam) is the regularization parameter, determining the trade-off between the fitting error minimization and smoothness of the estimated function. sigma^2 (sig2) is the kernel function parameter of the RBF kernel. The parameters and the variables relevant for the LS-SVM are passed as one cell. This cell allows for consistent default handling of LS-SVM parameters and syntactical grouping of related arguments.
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Size: 12288 |
Author: hifa |
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Description: 一个用BoW|Pyramid BoW+SVM进行图像分类的Matlab Demo-A use BoW | Pyramid BoW+ SVM for image classification Matlab Demo
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Size: 3507200 |
Author: 邓旭 |
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Description: HOG+SVM人脸识别演示程序 使用梯度直方图特征与SVM分类器-face detection demo program using HOG SVM
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Size: 7041024 |
Author: Charles |
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Description: osu-svm是一个基于libsvm的matlab工具箱,分为c-svm和nu-svm,本质上差不多,c-svm中c的范围是1到正无穷,nu-svm中nu的范围是0到1,nu是错分(ComputeSampEnSVM_Toolbox\osu_svm3.00\cmap.mat
SVM_Toolbox\osu_svm3.00\Contents.m
SVM_Toolbox\osu_svm3.00\demo\c_clademo.m
SVM_Toolbox\osu_svm3.00\demo\c_lindemo.m
SVM_Toolbox\osu_svm3.00\demo\c_poldemo.m
SVM_Toolbox\osu_svm3.00\demo\c_rbfdemo.m
SVM_Toolbox\osu_svm3.00\demo\c_svcdemo.m
SVM_Toolbox\osu_svm3.00\demo\DemoData_class.mat
SVM_Toolbox\osu_svm3.00\demo\DemoData_test.mat
SVM_Toolbox\osu_svm3.00\demo\DemoData_train.mat
SVM_Toolbox\osu_svm3.00\d)
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Size: 5623808 |
Author: LYH0510
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