Description: 基于kernel pca的非线性降维算法，原文发表于神经计算杂志上，有兴趣者可以先看论文。-PCA-based kernel of nonlinear reduced dimension algorithm, the original published in the Journal of neural computation, those interested can read papers. Platform: |
Size: 1024 |
Author:武旗 |
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Description: 用EM算法估计PCA参数，效果比传统的PCA要好，原文发表于神经计算杂志上，有兴趣者可以先看论文。-using EM algorithm parameters estimated PCA results than the traditional PCA better, in the language of neural computation published in the magazine, those who are interested can read papers. Platform: |
Size: 159744 |
Author:武旗 |
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Description: 用主成分分析与神经网络进行人脸的识别
文件是整个的ＭＡＴＬＡＢ数据文件-using principal component analysis and neural networks face identification document is the entire data file MATLAB Platform: |
Size: 32768 |
Author: |
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Description: Simon Haykin的 《Neural NetWorks》例子原码，相当经典。相信很有用，特别SVM PCA等-Simon Haykin "Neural NetWorks" examples of the original code, very classic. I believe very useful, especially in such SVM PCA Platform: |
Size: 266240 |
Author:尹明 |
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Description: 基于神经元网络和 Linear Auto-Associative memory 和pca的人脸识别程序。-based on neural network and Linear Auto-Associative memory pca and face recognition procedures. Platform: |
Size: 5120 |
Author:王大宝 |
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Description: 基于PCA和神经网络的人脸识别方法研究方面的资料，希望对你们有帮助-PCA and neural network-based face recognition method of information, in the hope that you have to help Platform: |
Size: 268288 |
Author:lidandan |
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Description: 将pca和elman神经网络结合起来，找到了主要的变量-The PCA and the Elman neural network to combine to find the main variables Platform: |
Size: 1024 |
Author:王娆芬 |
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Description: 用基于Oja准则的PCA神经网络方法实现MUSIC算法，完成DOA估计 -Oja-based guidelines for the PCA neural network method MUSIC algorithm, the completion of the estimated DOA Platform: |
Size: 2048 |
Author:金江 |
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Description: We propose an algorithm for facial expression recognition which can classify the given image into one of the seven basic facial expression categories (happiness, sadness, fear, surprise, anger, disgust and neutral). PCA is used for dimensionality reduction in input data while retaining those characteristics of the data set that contribute most to its variance, by keeping lower-order principal components and ignoring higher-order ones. Such low-order components contain the "most important" aspects of the data. The extracted feature vectors in the reduced space are used to train the supervised Neural Network classifier. This approach results extremely powerful because it does not require the detection of any reference point or node grid. The proposed method is fast and can be used for real-time applications. Platform: |
Size: 21504 |
Author:mhm |
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