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Description: 经典的人工智能问题 - iris数据分析问题。通过设计三层bp神经网络对花朵数据进行分类识别，并达到了很好的效果。-classic AI problem- iris data analysis problems. Three-bp through the design of neural networks classify data flower identification, and to achieve good results.
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Author: lyx |
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Description: matlab实现pca算法，基于orl人脸数据库，有很高的识别率-matlab achieve the PCA algorithm, based on the ORL face database, a high recognition rate
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Size: 1024 |
Author: 秦晔 |
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Description: This a demonstration of how one can use PCA to classify a 2D data set-This is a demonstration of how one can use PCA to classify a 2D data set
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Size: 1024 |
Author: Pista |
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Description: 2D PCA IS METHOD USED FOR IRIS AND FACE RECOGNITION AN INTERSETING METHOD
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Size: 1024 |
Author: vinayak |
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Description: 一个用PCA和LDA降维，并用knn分类的人脸识别例程-A dimension reduction using PCA and LDA, and face recognition with the knn classification routines
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Author: taoda |
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Description: PCA Algoritms Iris Data test
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Author: ood |
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Description: 模式识别作业-完全自编仿真程序。先用PCA对IRIS数据集进行降维，然后用最小错误法对降维的数据进行分类。压缩包中既包括matlab源代码，又有自己写的报告，还有.MAT格式的IRIS数据集用作程序调用。程序有详细注释，很容易懂。最后结果输出到txt文件中。-Pattern recognition operations- completely self simulation program. First on the IRIS data set with PCA dimension reduction, and then with the minimum error method to classify the data dimension reduction. Both compressed package matlab source code, but also write their own reports, as well. MAT format, the program calls for IRIS data set. Procedures detailed notes, it is easy to understand. Finally, the output to a txt file.
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Size: 94208 |
Author: yumingwei |
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Description: 用pca分类器将Iris数据分开，并分析pca分类器的特点-Classifier with the Iris data pca separate and analyze the characteristics of classification pca
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Size: 1024 |
Author: 小丽 |
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Description: 基于PCA的iris数据集分类，matlab实现，大家共同学习。-Iris data set the PCA-based classification, the Matlab implementation, we learn together.
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Size: 2048 |
Author: kangyan |
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Description: in this program I have deduced dimension of iris data with PCA
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Size: 1024 |
Author: amidi |
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Description: Iris 数据集的pca降维，先对Iris数据集进行特征提取，数据归一化，并将结果用图像显示出来。-Iris dataset pca dimensionality reduction
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Size: 37888 |
Author: chou |
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Description: The ear, as a biometric, has been given less
attention, compared to other biometrics such as fingerprint,
face and iris. Since it is a relatively new biometric, no
commercial applications involving ear recognition are
available. Intensive research in this field is thus required to
determine the feasibility of this biometric. In medical field,
especially in case of accidents and death, where face of patients
cannot be recognized, the use of ear can be helpful. In this
work, yet another method of recognizing people through their
ears is presented. Local Binary Patterns (LBP) is used as
features and the results are compared with that of Principal
Components Analysis (PCA). LBPhas a high discriminative
power, tolerance against globalillumination changes and low
computational load. Experiments were done on the Indian
Institute of Technology (IIT) Delhi ear image database and
results show that LBP yields a recognition rate of 93 while
PCA gives only 85 .-The ear, as a biometric, has been given less
attention, compared to other biometrics such as fingerprint,
face and iris. Since it is a relatively new biometric, no
commercial applications involving ear recognition are
available. Intensive research in this field is thus required to
determine the feasibility of this biometric. In medical field,
especially in case of accidents and death, where face of patients
cannot be recognized, the use of ear can be helpful. In this
work, yet another method of recognizing people through their
ears is presented. Local Binary Patterns (LBP) is used as
features and the results are compared with that of Principal
Components Analysis (PCA). LBPhas a high discriminative
power, tolerance against globalillumination changes and low
computational load. Experiments were done on the Indian
Institute of Technology (IIT) Delhi ear image database and
results show that LBP yields a recognition rate of 93 while
PCA gives only 85 .
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Author: krish |
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Description: (1)Bayes分类
已知N=9， =3，n=2，C=3,问x= 应属于哪一类？
(2)聚类
使用c－均值聚类算法在IRIS数据上进行聚类分析
(3)鉴别分析
在ORL或Yale标准人脸数据库上完成模式识别任务。
用PCA与基于核的PCA（KPCA）方法完成人脸图像的重构与识别试验。-(1) Bayes classification
Known N = 9, = 3, n = 2, C = 3, x = should ask which category?
(2) Cluster
C-means clustering algorithm using cluster analysis on IRIS data
(3) discriminant analysis
Pattern recognition tasks completed on ORL face database standard or Yale.
With PCA and nuclear-based PCA (KPCA) method to complete the reconstruction of the face image recognition test.
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Size: 7168 |
Author: 刘宏 |
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Description: Compression by PCA of Gaussian and Iris data
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Author: Bendjama |
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Description: Continuous efforts have been made to process degraded iris images for enhancement of the iris
recognition performance in unconstrained situations. Recently, many researchers have focused on
developing the iris segmentation techniques, which can deal with iris images in a non-cooperative
environment where the probability of acquiring unideal iris images is very high due to gaze deviation,
noise, blurring, and occlusion by eyelashes, eyelids, glasses,
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Author: basma_ammour |
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Description: 这是用pca进行数据处理的，数据分析原理与实验的作业，里面有iris数据，可以运行。-this is a program for pca ,its data is iris. it is a homework for data analysis .
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Size: 24576 |
Author: 赵鹏 |
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Description: PCA基于Iris数据集，用于统计学习作业。是非常好用且鲁棒的方法。有利于学习统计学习PCA和matlab。-PCA based on Iris data set, used in statistical learning assignments.It is very convenient and robust method.Is advantageous to the study of statistical learning PCA and matlab.
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Size: 2048 |
Author: 刘磊 |
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Description: IRIS PCA IDIYNDIY EOD I DIUHE SUY
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Author: alex |
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Description: 用PCA算法对iris数据集进行主成分分析(The PCA algorithm is used to analyze the iris data set)
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Size: 2048 |
Author: redeemer
|
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Description: 内涵PCA降维;SMOTE插值;t-SNE降维等算法的随机森林算法，以及鸢尾花数据集，有利于新手或者工程性实验借鉴~(Connotative PCA dimensionality reduction; SMOTE interpolation; t-SNE dimensionality reduction algorithms such as random forest algorithm, as well as iris data sets, is conducive to novice or engineering experiment reference ~)
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Author: SaoYear |
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