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多分类支持向量机实现方法的分析比较:一对一、一对多和DAG的对比,比较专业,-more SVM method of comparison : one-to-one, one-to-many and DAG contrast, more professional-huh
Update : 2008-10-13 Size : 166.75kb Publisher : 刘国亮

多分类支持向量机实现方法的分析比较:一对一、一对多和DAG的对比,比较专业,-more SVM method of comparison : one-to-one, one-to-many and DAG contrast, more professional-huh
Update : 2025-02-19 Size : 166kb Publisher : 刘国亮

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it is the best paper that discribe the supporrt vector machine for multiple classes.
Update : 2025-02-19 Size : 166kb Publisher : yashpal

台湾林智仁等对如何使用SVM进行多分类的理论、算法以及实验的介绍-Taiwan' s Lin Zhiren, etc. on how to use SVM for multi-classification of the theory, algorithms and experimental introduction of
Update : 2025-02-19 Size : 113kb Publisher :

一个多分类支持向量机的文件,可直接运行,并且可以多数据进行多重分类,十分好用-A multi-class support vector machine files can be directly run, and can make multiple multiple data classification, is very easy to use
Update : 2025-02-19 Size : 856kb Publisher : Fairy Wang

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使用SVM进行多类的一对一分类,有利于初学者对SVM进行快速入门-The function of the code is to classify multiple classes using one-to-one SVM, which must be very useful for beginners.
Update : 2025-02-19 Size : 506kb Publisher :

Multi SVM Classifier Matlab Code
Update : 2025-02-19 Size : 2kb Publisher : Mohammad

svm algorithm implementation
Update : 2025-02-19 Size : 2kb Publisher : suresh

audio classification using SVM for 5 classes
Update : 2025-02-19 Size : 1.61mb Publisher : selva

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Matlab code for multiclass Classification using inbuilt SVMClassify function.
Update : 2025-02-19 Size : 1kb Publisher : vimal

Matlab code for multiclass svm
Update : 2025-02-19 Size : 1kb Publisher : manu

mutli-SVM classifier
Update : 2025-02-19 Size : 1kb Publisher : 学泊位

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MULTICLASS SVM FOR FACE EXPRESSION RECOGNITION
Update : 2025-02-19 Size : 1kb Publisher : geethu g s

multi class support vector machine
Update : 2025-02-19 Size : 2kb Publisher : arwaraad

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tish code is mulltisvm and sue in classification
Update : 2025-02-19 Size : 1kb Publisher : roya

Frontal views of all subjects are videotaped under constant illumination using fixed light sources, and none of the subjects wear eyeglasses. These constraints are imposed to minimize optical flow degradation. Previously untrained subjects are video recorded performing a series of expressions, and the image sequences are coded by certified FACS coders. Facial expressions are analyzed in digitized image sequences of arbitrary length (expression sequences neutral to peak vary 9 to 44 frames). 60 subjects, both male and female, the larger database were used in this study. The study includes more than 260 image sequences and 5000 images. Subjects ranged in age (18-35) and ethnicity (Caucasian, African- American, and Asian/Indian).-Frontal views of all subjects are videotaped under constant illumination using fixed light sources, and none of the subjects wear eyeglasses. These constraints are imposed to minimize optical flow degradation. Previously untrained subjects are video recorded performing a series of expressions, and the image sequences are coded by certified FACS coders. Facial expressions are analyzed in digitized image sequences of arbitrary length (expression sequences neutral to peak vary 9 to 44 frames). 60 subjects, both male and female, the larger database were used in this study. The study includes more than 260 image sequences and 5000 images. Subjects ranged in age (18-35) and ethnicity (Caucasian, African- American, and Asian/Indian).
Update : 2025-02-19 Size : 1kb Publisher : qussai

examples of SVM, PCA , MultiSVM, Feature extraction, kernel function
Update : 2025-02-19 Size : 3.85mb Publisher : pinku11111

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multi svm support vector machines
Update : 2025-02-19 Size : 2kb Publisher : khalili
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