Description: 含有Matlab的各种程序和代码，包括图像分割、模式识别、文字识别等。并且含有初学者入门的代码。-Matlab contains the various procedures and code, including image segmentation, pattern recognition, text recognition. And contain the code entry for beginners. Platform: |
Size: 88064 |
Author:萧然 |
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Description: 关于支持向量机的Matlab源代码，用于模式识别和非线性问题的程序。对于初学和不想具体了解支持向量机的很有用。-On Support Vector Machines Matlab source code for pattern recognition problems and nonlinear procedures. For beginner and do not want a specific understanding of support vector machine useful. Platform: |
Size: 124928 |
Author:kaku |
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Description: 详细说明了如何实现基于bp神经网络的手写数字识别。神经网络对于参数的设置是敏感的，尤其是隐藏层的单元个数，本文列出了一系列bp神经网络的应用的参数设置。结果表明，可以实现较好的模式识别功能-Detailed description of how to realize bp neural network-based handwritten numeral recognition. Neural network for parameter setting is sensitive, especially the number of hidden layer units, the paper sets out a series of bp neural network applications, parameters setting. The results show that pattern recognition can be achieved better functional Platform: |
Size: 1120256 |
Author:胡存英 |
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Description: 离散小波变换，然后主成分分析进行数据降维，用于模式识别，如人脸识别，掌纹，表情，指纹等识别。-Discrete wavelet transform, and then principal component analysis for data dimensionality reduction for pattern recognition, such as face recognition, palmprint, face, fingerprint and other identification. Platform: |
Size: 16384 |
Author:陈浩 |
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Description: 离散余弦变换，然后主成分分析进行数据降维，用于模式识别，如人脸识别，掌纹，表情，指纹等识别。-Discrete cosine transform, and then principal component analysis for data dimensionality reduction for pattern recognition, such as face recognition, palmprint, face, fingerprint and other identification. Platform: |
Size: 16384 |
Author:陈浩 |
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Description: These are the codes in "A note on two-dimensional linear discrimant analysis", Pattern Recognition Letter
In this paper, we show that the discriminant power of two-dimensional discriminant analysis is not stronger than that of LDA under the assumption that the same dimensionality is considered. In experimental parts, on one hand, we confirm the validity of our claim and show the matrix-based methods are not always better than vector-based methods in the small sample size problem on the other hand, we compare several distance measures when the feature matrices and feature vectors are adopted.
Platform: |
Size: 12288 |
Author:ruan |
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Description: 一个强大的统计模式识别工具箱，包含高斯分类器，高斯混合模型，主成分分析，支持向量机等常见分类方法。-A powerful statistical pattern recognition toolbox, including the Gaussian classifier, Gaussian mixture model, principal component analysis, support vector machines and other common classification methods. Platform: |
Size: 1253376 |
Author:何威 |
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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: |
Size: 4271104 |
Author:查日东 |
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Description: 使用高斯混合模型（GMM)模拟模式识别的源代码-The use of Gaussian mixture model (GMM) Analog Pattern Recognition of the source code Platform: |
Size: 1024 |
Author:qjc |
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Description: 使用高斯模型对威斯康辛州大学医学院长期乳腺癌数据进行了贝叶斯模式识别。识别率为95以上，可以作为模式识别的重要案例。-Gaussian model using the University of Wisconsin School of Medicine conducted a long-term breast cancer data Bayesian pattern recognition. Recognition rate is above 95 can be regarded as an important case of pattern recognition. Platform: |
Size: 622592 |
Author:苏冠华 |
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