Description: 模式识别中线性判别分类器的C++源码，非常容易集成和使用！-pattern recognition linear discriminant classifier C source code, and is easy to integrate and use! Platform: |
Size: 3603 |
Author:sjtu |
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Description: 模式识别中线性判别分类器的C++源码，非常容易集成和使用！-pattern recognition linear discriminant classifier C source code, and is easy to integrate and use! Platform: |
Size: 3072 |
Author: |
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Description: LINEAR DISCRIMINANT CLASSIFIER 分类器, 用C语言写的.大家一起参考一下. -LINEAR DISCRIMINANT CLASSIFIER classifier, using the C language. U.S. with reference. Platform: |
Size: 10240 |
Author:sj2ac |
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Description: This Two-Category Classifier Using Discriminant Functions to
separeate two classes. The Classifier is designed on classes which
has two feature vectors and other case it has one feature vector. Platform: |
Size: 17408 |
Author:段西尧 |
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Description: 该程序包实现了几个常用的模式识别分类器算法，包括K近邻分类器KNN、线性判别方程LDF分类器、二次判别方程QDF分类器、RDA规则判别分析分类器、MQDF改进二次判别方程分类器、SVM支持向量机分类器。 主程序中还有接口调用举例，压缩包中还有两个测试数据集文件。-The package to achieve a number of commonly used pattern recognition classifier algorithms, including K neighbor classifier KNN, linear discriminant equation LDF classifier, quadratic discriminant equation QDF classifier, RDA rules of discriminant analysis classifier, MQDF improve the quadratic discriminant equation classifier, SVM Support Vector Machine classifier. Also call the main program interface, for example, compressed package there are two test data sets document. Platform: |
Size: 100352 |
Author:tangxiaojun |
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Description: Fisher线性判别分类器应用于IRIS数据集的例子-An example of Fisher linear discriminant Classifier applied on IRIS Data Set Platform: |
Size: 164864 |
Author:LikeTheBird |
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Description: The High Dimensional Discriminant Analysis (HDDA) toolbox contains an efficient supervised classifier for high-dimensional data. This classifier is based on Gaussian models adapted for high-dimensional data.
Reference: C. Bouveyron, S. Girard and C. Schmid, High Dimensional Discriminant Analysis, Communications in Statistics: Theory and Methods, vol. 36 (14), 2007.-The High Dimensional Discriminant Analysis (HDDA) toolbox contains an efficient supervised classifier for high-dimensional data. This classifier is based on Gaussian models adapted for high-dimensional data.
Reference: C. Bouveyron, S. Girard and C. Schmid, High Dimensional Discriminant Analysis, Communications in Statistics: Theory and Methods, vol. 36 (14), 2007. Platform: |
Size: 301056 |
Author:tra ba huy |
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Description: 通过用最小距离分类判别方法，用MATLAB程序找出最小距离分类判别时的识别界面，从而进行识别已知的两类训练样本，并分析其识别错误率。-By using minimum distance classifier discriminant method, using MATLAB program to find the minimum distance classifier recognition interface when the judge, which is known to identify the two types of training samples, and analyze the recognition error rate. Platform: |
Size: 4096 |
Author:lixue |
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Description: In this paper, we show how support vector machine (SVM) can be
employed as a powerful tool for $k$-nearest neighbor (kNN)
classifier. A novel multi-class dimensionality reduction approach,
Discriminant Analysis via Support Vectors (SVDA), is introduced by
using the SVM. The kernel mapping idea is used to derive the
non-linear version, Kernel Discriminant via Support Vectors (SVKD).
In SVDA, only support vectors are involved to obtain the
transformation matrix. Thus, the computational complexity can be
greatly reduced for kernel based feature extraction. Experiments
carried out on several standard databases show a clear improvement
on LDA-based recognition Platform: |
Size: 2048 |
Author:sofi |
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Description: 利用matlab实现的数值型贝叶斯分类器的源代码，可以用来分类或识别，很值得收藏-Using matlab to achieve numerical Bayesian classifier source code can be used to classification or identification, it is worthy of collection Platform: |
Size: 4096 |
Author:satanwings |
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Description: 提出了基于特征融合和模糊核判别分析(FKDA)的面部表情识别方法。首先，从每幅人脸图像中手工定
位34个基准点，作为面部表情图像的几何特征，同时采用Gabor小波变换方法对每幅表情图像进行变换，并提取基
准点处的Gabor小波系数值作为表情图像的Gabor特征；其次，利用典型相关分析技术对几何特征和Gabor特征进
行特征融合，作为表情识别的输人特征；然后，利用模糊核判别分析方法进一步提取表情的鉴别特征；最后，采用最
近邻分类器完成表情的分类识别。通过在JAFFE国际表情数据库和Ekman“面部表情图片”数据库上的实验，证实
了所提方法的有效性。-Proposed based on feature fusion and fuzzy kernel discriminant analysis (FKDA) facial expression recognition. First, face images of each piece of hand-set
Bit 34 basis points, as the geometric features of facial expression images, while using Gabor wavelet transform method to transform the images of each piece of expression, and extraction-based
Quasi-point of the Gabor wavelet coefficients, as Gabor features of facial expression image second, using canonical correlation analysis on the geometric features and Gabor features into
Line feature fusion, as expression recognition of input features then, using fuzzy kernel discriminant analysis method to extract and further identification features of expression Finally, the most
Neighbor classifier to complete expression of the classification. International expression by JAFFE database and Ekman "facial image" database on the experiment, confirmed
The proposed method. Platform: |
Size: 375808 |
Author:MJ |
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Description: 贝叶斯分类器实现多类识别，主要用于两类的识别-BAYES_CLASSIFIER function calculates the discriminant functions for
two classes. Platform: |
Size: 1024 |
Author:xingtao |
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Description: 基于Fisher线性判别的基因分类器的设计，里面有源程序-Fisher linear discriminant based on the gene classifier design, which has source code Platform: |
Size: 90112 |
Author: |
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Description: 此为模式识别中的Fisher线性判别方法求分类器。进行了男、女错误率和总错误率的统计。
全部程序流程如下：
1、读取FAMALE.TXT文件把身高或体重给数组，并求x1的样本均值m1和内离散度S1；
2、读取MALE.TXT文件把身高或体重给数组，并求x2的样本均值m2和内离散度S2；
3、求最佳变换向量和阀值点；
4、读取Test2.txt文件把对应的身高或体重给数组A并求A的样本数M；
5、把最佳变换向量和阀值点、x代入判别方程g（x）；
6、对本判别的错误率进行统计。-This is the pattern recognition method, to calculate the Fisher linear discriminant classifier .Were male , female and total error rate error rate statistics .
All program flow is as follows :
1 , read FAMALE.TXT file to height or weight to the array, and find the sample mean m1 x1 and internal dispersion S1
2 , read MALE.TXT file to height or weight to the array, and find the sample mean m2 and x2 within the dispersion S2
3 , for the best transformation vector and the threshold point
4 , read Test2.txt file to the corresponding height or weight requirements for the array A and A is the number of samples M
5 , the best transformation vector and the threshold point , x substituted into the discriminant equation g (x)
6 , determine the error rate of the statistics .
Platform: |
Size: 3072 |
Author:崔杉 |
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