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利用fisher线性判别分析进行数据降维-using linear discriminant analysis of data dimensionality reduction
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模式识别中的脱机字符识别，包括手写数字识别之Fisher线性判别，手写数字识别之模板匹配法，数字识别之神经网络法及细化算法。-pattern recognition of Offline Character Recognition, including handwritten digital identification Fisher Linear Discriminant. Handwritten identification template matching, digital identi
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脱机字符识别算法，包括手写数字识别之Fisher线性判别，手写数字识别之模板匹配法，数字识别之神经网络法，细化算法
-offline character recognition algorithms, including handwritten digital identification Fisher Linear Discriminant. Handwritten identification template matching, digital identification neura
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包括:
手写数字识别之Fisher线性判别
手写数字识别之模板匹配法
数字识别之神经网络法
细化算法-include : Handwritten identification Fisher Linear Discriminant handwritten digital identification template matching identification number of neural network method in Algorithm
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现有的代数特征的抽取方法绝大多数采用一维的方法，即首先将图像转换为一维向量，再用主分量分析（PCA）,Fisher线性鉴别分析（LDA）,Fisherfaces式核主分量分析（KPCA）等方法抽取特征，然后用适合的分类器分类。针对一维方法维数过高，计算量大，协方差矩阵常常是奇异矩阵等不足，提出了二维的图像特征抽取方法，计算量小，协方差矩阵一般是可逆的，且识别率较高。-existing algebra feature extraction method using a majority of th
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pca,用在人脸特征识别中,fisher线性判别
kpca,dwt2-pca used in facial recognition, fisher linear discriminant kpca, dwt2
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这是用fisher线形判别方法对手写数字进行判别，很有用-fisher linear discriminant method of handwritten figures for the discriminant and useful
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数字图像识别中的脱机字符识别：手写数字识别之Fisher线性判别、手写数字识别之模板匹配法、数字识别之神经网络法、细化算法。-Digital Image Recognition of Offline Character Recognition : handwritten digital identification Fisher Linear Discriminant. Handwritten identification template matching, digital identific
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脱机字符识别算法，包括手写数字识别之Fisher线性判别，手写数字识别之模板匹配法，数字识别之神经网络法，细化算法
-offline character recognition algorithms, including handwritten digital identification Fisher Linear Discriminant. Handwritten identification template matching, digital identification neura
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脱机字符识别:手写数字识别之Fisher线性判别,手写数字识别之模板匹配法,数字识别之神经网络法,细化算法-Offline Character Recognition : handwritten figures identifiable Fisher Linear Discriminant, handwritten figures identifiable template matching method, digital identification neural network, thinn
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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,
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该源码是手写数字的fisher线性判别。在VC6.0环境下运行，MFC图形化界面，适用于模式识别，人工智能。数字图像处理的参考,The source is a hand-written digit fisher linear discriminant. Run in VC6.0 environment, MFC graphical interface for pattern recognition, artificial intelligence. Digital image processin
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通过Fisher线性判别是否为数字，并识别手写数字。-Through the Fisher Linear Discriminant for figures and handwritten numeral recognition.
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该程序应用fisher线性判别对平面两类问题进行分类，应用迭代寻求最佳投影方向。-Application of the program on fisher linear discriminant plane issues two types of classification, the application of iterative searching for the best projection direction.
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张学工老师模式识别课程二次作业，用fisher线性判别对身高体重二维数据进行性别分类-Zhang engineering curriculum of secondary teachers pattern recognition operations, fisher linear discriminant with two-dimensional data on height and weight, gender-disaggregated
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、改写例程，编制用Fisher线性判别方法对三维数据求最优方向W的通用函数。
2、对下面表3-1样本数据中的类别ω1和ω2计算最优方向W。
3、画出最优方向W的直线，并标记出投影后的点在直线上的位置-Rewriting routine, the preparation of Fisher linear discriminant method using three-dimensional data on the direction of seeking the best general-
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针对多个特征指标的多传感器数据融合问题，将Fisher理论和多数投票法相结合进行数据融合来增加识别率。该方法首先通过Fisher理论得到多个判别函数，然后通过多数投票法继续对得到的判别进行分类得到最后的识别决策。该方法适合多个特征目标识别，计算简单。易于实现-Indicators for multiple features multi-sensor data fusion problem, Fisher majority vote of the Combination of theory and
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模式识别实验验程序
极大似然估计和Fisher线形判别分析-Experimental test procedures for pattern recognition maximum likelihood estimation and Fisher linear discriminant analysis
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3种模板匹配法实现的手写数字识别示例,包括手写数字识别之Fisher线性判别、手写数字识别之模板匹配法、细化算法-Three kinds of template matching to achieve the handwritten numeral recognition examples, including handwritten digit recognition of the Fisher linear discriminant, handwritten numeral recognit
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Fisher、H-K、Perceptron算法实现线性判别函数-Fisher, HK, Perceptron algorithm linear discriminant function
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