Search - generalization matlab code

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[Bio-Recognize] gda DL : 0: 通过核来泛化的判别分析（GDA）代码，MATLAB写的。-through nuclear generalization to the discriminant analysis (GDA) code, written in MATLAB.
Update : 2008-10-13 Size : 5.5kb Publisher : 申中华
[Bio-Recognize] gda DL : 0: 通过核来泛化的判别分析（GDA）代码，MATLAB写的。-through nuclear generalization to the discriminant analysis (GDA) code, written in MATLAB.
Update : 2025-02-17 Size : 5kb Publisher : 申中华
[ADO-ODBC] gmdh2 DL : 0: 数据分组处理算法GMDH源代码是自组织数据挖掘的核心算法，具有很强的泛化能力，相比回归分析法可以处理小样本数据-GMDH packet processing algorithms source code is the core of self-organizing data mining algorithm, which has strong generalization ability, compared with regression analysis can deal with small samples
Update : 2025-02-17 Size : 2kb Publisher : 张瑛
[OS program] code-(2) DL : 0: Using MATLAB tools for MLP NNs (e.g., newff, …), design a two-layer feed-forward neural network as a classifier to categorize the input geometric shapes. - The snapshot and bitmap of shapes are given: - Training shapes: shkt.bmp - Training patterns: trn.txt (each shape is in a 125*140 matrix) - Test shapes: shks.bmp - Test patterns: tsn.txt (each shape is in a 125*140 matrix) - Since the dimension of inputs is too high (17500-dimensional), it is not possible to apply them directly to the net. So, … . - Try the number of hidden neurons to be at least. - Do training of NN until all training patterns are truly classified. - To examine the generalization ability of your NN after training, a) Apply it to the test patterns and report the accuracies. b) Add p noise (p=5, 10, …, 75) to the training shapes (only degrade the black pixels of the shapes) and report in a plot the accuracy versus p.-Using MATLAB tools for MLP NNs (e.g., newff, …), design a two-layer feed-forward neural network as a classifier to categorize the input geometric shapes. - The snapshot and bitmap of shapes are given: - Training shapes: shkt.bmp - Training patterns: trn.txt (each shape is in a 125*140 matrix) - Test shapes: shks.bmp - Test patterns: tsn.txt (each shape is in a 125*140 matrix) - Since the dimension of inputs is too high (17500-dimensional), it is not possible to apply them directly to the net. So, … . - Try the number of hidden neurons to be at least. - Do training of NN until all training patterns are truly classified. - To examine the generalization ability of your NN after training, a) Apply it to the test patterns and report the accuracies. b) Add p noise (p=5, 10, …, 75) to the training shapes (only degrade the black pixels of the shapes) and report in a plot the accuracy versus p.
Update : 2025-02-17 Size : 3kb Publisher : fatemeh