Description: 开发环境:Matlab 简要说明:自组织特征映射模型(Self-Organizing feature Map)，认为一个神经网络接受外界输入模式时，将会分为不同的区域，各区域对输入模式具有不同的响应特征，同时这一过程是自动完成的。各神经元的连接权值具有一定的分布。最邻近的神经元互相刺激，而较远的神经元则相互抑制，更远一些的则具有较弱的刺激作用。自组织特征映射法是一种无教师的聚类方法。-development environment : Matlab Brief Description : Self-Organizing Map model (Self-Organizing Map feature), a neural network that external input mode, will be divided into different regions, the regional input to the model with different response characteristics and the process is automatic End %. The neurons connect with the right to a certain value of the distribution. Most neighboring neurons stimulate each other, distant neurons were mutual inhibition, the vision has a weaker stimulus. Self-organizing feature mapping method is a non-teachers clustering method. Platform: |
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Author:李洋 |
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Description: 实现了从wave中读取数据，并通过mfcc提取特征系数。再通过分裂法聚类，最后使用EM算法建立GMM。-Realize the data read from the wave, and through MFCC feature extraction coefficient. Clustering through secession law, and finally the use of EM algorithm for the establishment of GMM. Platform: |
Size: 5120 |
Author:gujunjun |
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Description: 熟悉三角形模糊数、中心及隶属函数表达式的概念。了解特征映射算法及统计中的 统计量的概念。利用聚类迭代算法建立 个三角形形式的隶属函数
-Familiar with the triangular fuzzy number, membership function centers and the concept of expression. Understand the feature mapping algorithm and statistics of the concept of statistics. Iterative use of clustering algorithms to establish a triangular form of membership function Platform: |
Size: 1024 |
Author:David |
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Description: 一种 较新的聚类算法 Dominant-set 的代码，包括聚类算法的代码和测试代码。该算法最大特点 就是基于图理论的 ，相对于Normalized Cut,计算复杂度低很多，况且能自动决定类的个数 -A relatively new clustering algorithm Dominant-set the code, including the clustering algorithm code and test code. Most prominent feature of the algorithm is based on graph theory, and compared with the Normalized Cut, much lower computational complexity, decision Moreover automatically the number of categories Platform: |
Size: 3072 |
Author:曾祥林 |
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Description: 协同模糊聚类建模通过特征选择和协同模糊聚类的模糊建模方法构建T-S模型，并用此模型对数据进行测试。-Collaborative fuzzy clustering modeling and collaboration through the feature selection fuzzy clustering TS fuzzy modeling method to build models and use this model of data for testing. Platform: |
Size: 3072 |
Author:zhangwenming |
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Description: 统计模式识别工具箱（Statistical Pattern Recognition Toolbox）包含：
1，Analysis of linear discriminant function
2，Feature extraction: Linear Discriminant Analysis
3，Probability distribution estimation and clustering
4，Support Vector and other Kernel Machines-
This section should give the reader a quick overview of the methods implemented in
STPRtool.
• Analysis of linear discriminant function: Perceptron algorithm and multiclass
modification. Kozinec’s algorithm. Fisher Linear Discriminant. A collection
of known algorithms solving the Generalized Anderson’s Task.
• Feature extraction: Linear Discriminant Analysis. Principal Component Analysis
(PCA). Kernel PCA. Greedy Kernel PCA. Generalized Discriminant Analysis.
• Probability distribution estimation and clustering: Gaussian Mixture
Models. Expectation-Maximization algorithm. Minimax probability estimation.
K-means clustering.
• Support Vector and other Kernel Machines: Sequential Minimal Optimizer
(SMO). Matlab Optimization toolbox based algorithms. Interface to the
SVMlight software. Decomposition approaches to train the Multi-class SVM classifiers.
Multi-class BSVM formulation trained by Kozinec’s algorithm, Mitchell-
Demyanov-Molozenov algorithm Platform: |
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Author:查日东 |
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Description: 介绍了一种非常实用的特征提取新方法，针对稀疏核主成分分析方法在特征提取中的不足, 提出了一种基于核K- 均值聚类的稀疏核主成分分析( Sparse KPCA) 的特征提取方法用于说话人识别。-Introduced a very useful new method of feature extraction for Sparse Kernel Principal Component Analysis in Feature Extraction of the lack of a kernel-based K-means clustering of sparse kernel principal component analysis (Sparse KPCA) of the feature extraction methods for speaker recognition. Platform: |
Size: 122880 |
Author:毋桂萍 |
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Description: K-meansK均值聚类在无监督的情况下选择图像特征的算法-K-meansK means clustering in the case of unsupervised image feature selection algorithm Platform: |
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Author:renli |
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Description: 图像特征提取的总结，用MATLAB模糊聚类算法进行图像分割，阀值分割及特征提取的资料和作业。-Summary of the image feature extraction, fuzzy clustering algorithm using MATLAB for image segmentation, threshold segmentation and feature extraction of data and operations. Platform: |
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Author:abcd0609 |
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Description: 本程序包括：论文SVM 用于基于块划分特征提取的图像分类，和相应的matlab实现其中图像划分以及特征提取、聚类均利用matlab6.5完成。
-The procedures include: paper by SVM for feature extraction based on block classification, and the corresponding realization of one image into matlab, and feature extraction, clustering were done using matlab6.5. Platform: |
Size: 173056 |
Author:darren |
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Description: 针对FCM算法的运行时间长和计算量大的问题，提出了改进的FCM算法，先将图像分割成窗口大小的子块，然后以子块为单位提取特征向量，用FCM聚类粗分割，然后对边缘子块，以像素为单位从新提取特征向量，进行细分割。分割后的结果提高了运行速度和分割精度。-For the FCM algorithm and the calculation of long run the problem of large proposed improved FCM algorithm, first image into blocks the size of the window, and then sub-block feature vector extraction unit, using FCM clustering coarse partition, and then block on the edge, in pixels from the new Feature Extraction, for fine segmentation. Improve the segmentation results after the speed and accuracy of segmentation. Platform: |
Size: 3078144 |
Author:殷皓君 |
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Description: 自组织特征映射模型(Self-Organizing feature Map)，认为一个神经网络接受外界输入模式时，将会分为不同的区域，各区域对输入模式具有不同的响应特征，同时这一过程是自动完成的。各神经元的连接权值具有一定的分布。最邻近的神经元互相刺激，而较远的神经元则相互抑制，更远一些的则具有较弱的刺激作用。自组织特征映射法是一种无教师的聚类方法。-Self-organizing maps model (Self-Organizing feature Map), that a neural network to accept outside input mode, will be divided into different regions, the regional input modes have different response characteristics, while the process is done automatically . The connection weights of neurons with a certain distribution. Nearest neurons stimulate each other, while distant neurons are mutually inhibitory, with a further some of the weaker stimulus. Self-organizing feature map method is a clustering method without teachers. Platform: |
Size: 1024 |
Author:yyt |
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Description: 谱聚类能够识别任意形状的样本空间且收敛于全局最优解，其基本思想是利用样本数据相似矩阵的进行特征分解后得到的特征向量进行聚类，程序进行了几种不同聚类算法的比较，包括Q矩阵聚类，kmeans聚类，第一特征分量聚类，第二广义特征分量聚类，公用数据生成和近邻矩阵生成(Spectral clustering can distinguish arbitrary sample space and converge to the global optimal solution, the basic idea is similar to matrix of eigenvectors obtained after decomposition of clustering using the sample data, procedures for the comparison of several different clustering algorithms, including Q matrix clustering, kmeans clustering, the first feature clustering, second generalized characteristic component clustering, public data generation and neighbor matrix generation) Platform: |
Size: 5120 |
Author:jadegem
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