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Description: 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
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Size: 170757 |
Author: 刘国亮 |
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Description: 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
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Size: 169984 |
Author: 刘国亮 |
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Description: it is the best paper that discribe the supporrt vector machine for multiple classes.
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Size: 169984 |
Author: yashpal |
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Description: 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
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Size: 115712 |
Author: 李 |
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Description: 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
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Size: 876544 |
Author: Fairy Wang |
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Description: 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.
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Size: 518144 |
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Description: Multi SVM Classifier Matlab Code
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Size: 2048 |
Author: Mohammad |
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Description: svm algorithm implementation
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Size: 2048 |
Author: suresh |
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Description: audio classification using SVM for 5 classes
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Size: 1690624 |
Author: selva |
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Description: Matlab code for multiclass Classification using inbuilt SVMClassify function.
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Size: 1024 |
Author: vimal |
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Description: Matlab code for multiclass svm
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Size: 1024 |
Author: manu |
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Description: mutli-SVM classifier
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Size: 1024 |
Author: 学泊位 |
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Description: MULTICLASS SVM FOR FACE EXPRESSION RECOGNITION
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Size: 1024 |
Author: geethu g s |
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Description: multi class support vector machine
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Size: 2048 |
Author: arwaraad |
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Description: tish code is mulltisvm and sue in classification
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Size: 1024 |
Author: roya |
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Description: 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).
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Size: 1024 |
Author: qussai |
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Description: examples of SVM, PCA , MultiSVM, Feature extraction, kernel function
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Size: 4041728 |
Author: pinku11111
|
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Description: multi svm
support vector machines
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Size: 2048 |
Author: khalili
|
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