Description: 统计模式识别、线性或非线性回归以及人工神经网络等方法是数据挖掘的有效工具，支持向量分类（support vector classification，简称SVC）算法是一个很有发展前景的方向。-Statistical pattern recognition, linear or nonlinear regression and artificial neural network approach is an effective tool for data mining, support vector classification (support vector classification, referred to as SVC) algorithm is a promising direction. Platform: |
Size: 10240 |
Author:xs |
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Description: 本人编写的incremental 随机神经元网络算法，该算法最大的特点是可以保证approximation特性，而且速度快效果不错，可以作为学术上的比较和分析。目前只适合benchmark的regression问题。
具体效果可参考
G.-B. Huang, L. Chen and C.-K. Siew, “Universal Approximation Using Incremental Constructive Feedforward Networks with Random Hidden Nodes”, IEEE Transactions on Neural Networks, vol. 17, no. 4, pp. 879-892, 2006.
-I prepared by incremental random neural network algorithm, which is characterized by the largest approximation properties can be guaranteed, and fast good results can be used as an academic comparison and analysis. The current benchmark is only suitable for the regression problem. Specific effects may refer G.-B. Huang, L. Chen and C.-K. Siew, Platform: |
Size: 2048 |
Author:chenlei |
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Description: BP网络的应用： BP神经网络用于分类与回归， 使用matlab打开-Application of BP Network: BP neural network for classification and regression, the use of matlab to open Platform: |
Size: 3072 |
Author:ycs |
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Description: RBF神经网络用于分类与回归，非常实用，强烈建议下载-RBF neural network for classification and regression, very practical, it is strongly recommended to download Platform: |
Size: 2048 |
Author:leo |
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Description: 支持向量机和BP神经网络虽然都可以用来做非线性回归，但它们所基于的理论基础不同，回归的机理也不相同。支持向量机基于结构风险最小化理论，普遍认为其泛化能力要比神经网络的强。为了验证这种观点，本文编写了支持向量机非线性回归的通用Matlab程序和基于神经网络工具箱的BP神经网络仿真模块，仿真结果证实，支持向量机做非线性回归不仅泛化能力强于BP网络，而且能避免神经网络的固有缺陷——训练结果不稳定。
-Support Vector Machine and BP neural network, even though there can be used to make non-linear regression, but they are based on the theoretical basis for the different, the mechanism of regression is not the same. Support vector machine based on structural risk minimization theory, generally considered the generalization ability of neural networks than the strong. To test this view, the paper prepared by non-linear regression support vector machine procedures and based on a common Matlab neural network toolbox of BP neural network Platform: |
Size: 3072 |
Author:孙准 |
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Description: 神经网络用于分类与回归,先产生训练样本与测试样本，每一列为一个样本- neural network for classification and regression, first create the training samples and test samples, each as a sample of Platform: |
Size: 3072 |
Author:zhongjinqin |
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Description: RBF神经网络用于分类与回归，提供了一个学习神经网络的平台，对大家有很大帮助。-RBF neural network for classification and regression, neural networks provide a learning platform for all of us much help. Platform: |
Size: 2048 |
Author:孙天号 |
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Description: rbf神经网络用于函数的回归，能根据已知的系写点对函数进行分析，达到预期目标。-rbf neural network for function regression, according to the known system of writing points to the function, to achieve the desired objectives. Platform: |
Size: 2048 |
Author:杨宁 |
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Description: 径向基神经网络的应用，回归分析和分类分析程序。有一定的参考意义。-Application of RBF neural network, regression analysis and classification of procedures. Have some reference value. Platform: |
Size: 1024 |
Author:lihang |
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Description: BP神经网络训练程序，可以用来做各种计算，如线性拟合-BP neural network training procedures can be used for various calculations, such as linear regression, etc. Platform: |
Size: 1024 |
Author:yanfei |
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