搜索资源列表
xiaobofenxi-PCA
- 文章阐释了基于小波分析的改进型的PCA算法在人脸识别中的应用和具体实现。-The article illustrates the application and concrete realization of improved PCA algorithm based on wavelet analysis in face recognition.
Data-Mining-Concepts-and-Techniques
- 数据挖掘：KDD 过程，不同类型数据的距离度量方式，小波 PCA 属性子集选择-KMean KModoid BIRCH CHEMELEON DBSCAN OPTICE STING CLIQUE EM SCAN
7250
- 时间序列数据分析中的梅林变换工具，插值与拟合的matlab实现，Gabor小波变换与PCA的人脸识别代码。- Time series data analysis Mellin transform tool, Interpolation and fitting matlab implementation, Gabor wavelet transform and PCA face recognition code.
5207
- 最小均方误差（MMSE）的算法，Gabor小波变换与PCA的人脸识别代码，模式识别中的bayes判别分析算法。- Minimum mean square error (MMSE) algorithm, Gabor wavelet transform and PCA face recognition code, Pattern Recognition bayes discriminant analysis algorithm.
amhai
- Gabor小波变换与PCA的人脸识别代码，大学数值分析算法，matlab小波分析程序。- Gabor wavelet transform and PCA face recognition code, University of numerical analysis algorithms, matlab wavelet analysis program.
dajbm
- 利用matlab针对图像进行马氏距离计算 ,实现了图像的灰度化并进一步用于视频监视控，Gabor小波变换与PCA的人脸识别代码。- Using matlab to calculate the Mahalanobis distance for the image, Achieve a grayscale image and further control for video surveillance, Gabor wavelet transform and PCA face recognition
jiu-V7.4
- 完整的基于HMM的语音识别系统，Gabor小波变换与PCA的人脸识别代码，利用最小二乘算法实现对三维平面的拟合。- Complete HMM-based speech recognition system, Gabor wavelet transform and PCA face recognition code, Least-squares algorithm to fit a three-dimensional plane.
6280
- 有PMUSIC 校正前和校正后的比较，Gabor小波变换与PCA的人脸识别代码，本程序的性能已经达到较高水平。- A relatively before correction and after correction PMUSIC, Gabor wavelet transform and PCA face recognition code, The performance of the program has reached a high level.
puakv
- 用于时频分析算法，Gabor小波变换与PCA的人脸识别代码，关于神经网络控制。- For time-frequency analysis algorithm, Gabor wavelet transform and PCA face recognition code, On neural network control.
nn655
- IDW距离反比加权方法，Gabor小波变换与PCA的人脸识别代码，采用加权网络中节点强度和权重都是幂率分布的模型。- IDW inverse distance weighting method, Gabor wavelet transform and PCA face recognition code, Using weighted model nodes in the network strength and weight are power law distribution.