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Description: 这个程序是matlab编写的支持向量程序，主要由于预测。-This procedure is prepared matlab support vector procedure, mainly due to prediction.
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Size: 425984 |
Author: mawenguo |
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Description: knn文本分类算法，大家都来下载吧
免费的啊-KNN text categorization algorithms, we all to download it for free, ah
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Size: 4962304 |
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Description: 一个很不错的开源的SVM软件，可以自由的修改源代码-A very good open-source SVM software, are free to modify the source code
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Size: 21504 |
Author: 朱睿 |
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Description: Vapnik-Cheervonenkis (VC) Dimension
Support Vector Machines
SVM Applications
Committee machines
PAC Learning
Boosting
“No Free Lunch” Theorem-Vapnik-Cheervonenkis (VC) Dimension Support Vector Machines SVM Applications Committee machines PAC Learning Boosting " No Free Lunch" Theorem
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Size: 1167360 |
Author: Shelly Chen |
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Description: 支持向量机方法是建立在统计学习理论的VC 维理论和结构风险最小原理基础上的，根据有限的样本信息在模型的复杂性（即对特定训练样本的学习精度，Accuracy）和学习能力（即无错误地识别任意样本的能力）之间寻求最佳折衷，以期获得最好的推广能力[14]（或称泛化能力）。 -SVM is based on statistical learning theory and the theory of VC dimension based on structural risk minimization principle, according to the limited sample of information in the model complexity (ie, training samples of a specific learning accuracy, Accuracy) and learning ability (ie, error-free samples to identify any capacity) to find the best compromise between, in order to obtain the best generalization ability [14] (or generalization).
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Size: 284672 |
Author: amma |
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Description: 本书介绍的支持向量机方法，是建立在统计学习理论的VC 维理论和结构风险最小原理基础上的，根据有限的样本信息在模型的复杂性（即对特定训练样本的学习精度）和学习能力（即无错误地识别任意样本的能力）之间寻求最佳折衷，以期获得最好的推广能力 。-This book introduces the support vector machine method is based on statistical learning theory, VC dimension and structural risk minimization theory based on the principle, according to the limited sample of information in the complexity of the model (ie, training samples of a specific learning accuracy) and learning capacity (ie, error-free samples to identify any capacity) to find the best compromise between, in order to obtain the best generalization ability.
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Size: 798720 |
Author: 周星星 |
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Description: python语言写的SVm分类器，自己写的，完全可用！-python language written SVm classifier, write your own, completely free!
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Size: 2048 |
Author: yankl |
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Description: SVM实现数据分类，可自行选取核函数，用于图像分割操作。程序可自动训练分类平面。-SVM for data classification, are free to select the kernel function，For image segmentation operation. The program can automatically train classifiers plane.
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Size: 68608 |
Author: 小哥 |
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Description: LIBSVM是台湾大学林智仁(Lin Chih-Jen)副教授等开发设计的一个简单、易于使用和快速有效的SVM模式识别与回归的软件包，他不但提供了编译好的可在Windows系列系统的执行文件，还提供了源代码，方便改进、修改以及在其它操作系统上应用；该软件对SVM所涉及的参数调节相对比较少，提供了很多的默认参数，利用这些默认参数可以解决很多问题；并提供了交互检验(Cross Validation)的功能。该软件包可在http://www.csie.ntu.edu.tw/~cjlin/免费获得。该软件可以解决C-SVM、ν-SVM、ε-SVR和ν-SVR等问题，包括基于一对一算法的多类模式识别问题-The LIBSVM Is Taiwan University Chih- Jen Lin (Lin Chih-Jen) Associate Professor of the development and design of a simple, easy to use and fast and efficient SVM pattern recognition and regression package, he not only compiled perform file system in the Windows series also provides source code to facilitate the improvement modifications and applications on other operating systems the software adjust the parameters involved in SVM is relatively small, a lot of the default parameters, use these default parameters can solve a lot of problems and provide cross-validation (Cross Validation) function. The package can http://www.csie.ntu.edu.tw/ ~ cjlin/free access. The software can solve the problem of the C-SVM, ν-SVM, ε-SVR and ν-SVR, including the multi-class pattern recognition problem based on a one-to-one algorithm
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Size: 671744 |
Author: 王炜豪 |
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Description: SVM神经网络中的参数优化 -如何更好的提升分类器的性能 绝对可以无错运行-SVM neural network classifier parameter optimization performance improvement - how to better the absolute can be error free operation
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Size: 299008 |
Author: luofei |
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Description: SVM神经网络中的参数优化 -如何更好的提升分类器的性能 绝对可以无错运行-SVM neural network classifier parameter optimization performance improvement - how to better the absolute can be error free operation
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Size: 4096 |
Author: luofei |
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Description: The Pattern Recognition Toolbox (PRT) for MATLAB (tm) is a framework of pattern recognition and machine learning tools that are powerful, expressive, and easy to use.
Create a data set your data (X ~ N x F) and labels (Y ~ N x 1):
ds = prtDataSetClass(X,Y)
and run Z-score normalization + an SVM:
algo = prtPreProcZmuv + prtClassLibSvm
dsOut = algo.kfolds(ds)
And score the results:
prtScoreRoc(dsOut)
It s that easy. It s free. Have fun.-The Pattern Recognition Toolbox (PRT) for MATLAB (tm) is a framework of pattern recognition and machine learning tools that are powerful, expressive, and easy to use.
Create a data set your data (X ~ N x F) and labels (Y ~ N x 1):
ds = prtDataSetClass(X,Y)
and run Z-score normalization+ an SVM:
algo = prtPreProcZmuv+ prtClassLibSvm
dsOut = algo.kfolds(ds)
And score the results:
prtScoreRoc(dsOut)
It s that easy. It s free. Have fun.
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Size: 20184064 |
Author: 田田 |
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