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Description: 算法介绍
矩阵求逆在程序中很常见，主要应用于求Billboard矩阵。按照定义的计算方法乘法运算，严重影响了性能。在需要大量Billboard矩阵运算时，矩阵求逆的优化能极大提高性能。这里要介绍的矩阵求逆算法称为全选主元高斯-约旦法。
高斯-约旦法（全选主元）求逆的步骤如下：
首先，对于 k 从 0 到 n - 1 作如下几步：
从第 k 行、第 k 列开始的右下角子阵中选取绝对值最大的元素，并记住次元素所在的行号和列号，在通过行交换和列交换将它交换到主元素位置上。这一步称为全选主元。
m(k, k) = 1 / m(k, k)
m(k, j) = m(k, j) * m(k, k)，j = 0, 1, ..., n-1；j != k
m(i, j) = m(i, j) - m(i, k) * m(k, j)，i, j = 0, 1, ..., n-1；i, j != k
m(i, k) = -m(i, k) * m(k, k)，i = 0, 1, ..., n-1；i != k
最后，根据在全选主元过程中所记录的行、列交换的信息进行恢复，恢复的原则如下：在全选主元过程中，先交换的行（列）后进行恢复；原来的行（列）交换用列（行）交换来恢复。-algorithm introduced in the matrix inversion process is very common, which are mainly used for Billboard matrix. In accordance with the definition of the method of calculating multiplication, seriously affecting the performance. The need for a large number of Billboard matrix operations, matrix inversion optimization can significantly improve performance. Here we introduce the matrix inversion algorithm called full-elected PCA Gauss-Jordan and France. Gauss-Jordan and France (all elected PCA) inversion of the following steps : First, for k from 0 to n-1 for the following steps : from the first trip k, k started out the bottom right corner Subarray largest absolute selected elements, and element remember meeting the line and out, the adoption OK exchange and the exchange out of its exchange
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Author: 刘亮 |
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Description: K近邻法的matlab程序，发现大家都在找它！-K-nearest neighbor method of Matlab procedures, I found that we all have to find it!
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Description: K-NN 分类器, 用C语言写的.大家一起参考一下. -K-NN classifier, using the C language. U.S. with reference.
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Description: k-means算法。包括一个测试的4类的测试数据集。-k-means algorithm. Including a test of the 4 types of test data sets.
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Description: 利用传统PCA方法进行人脸识别的算法，人脸库为Yale人脸库，主成分分析方法（PCA）是基于K-L变换的统计学方法，K-L变换是数据压缩领域里的一种最优正交变换。-err
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Description: knn 方法为k均值聚类用于数据点的分类-KNN method for k-means clustering for the classification of data points
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Description: K最近邻分类的代码，附有输入输出和程序使用说明。-K nearest neighbor classification code, with input and output and procedures for use.
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Description: 用JAVA语言实现的经典聚类算法k-means，聚类与分类不同，它是无监督的过程，-JAVA language used to achieve a classic clustering algorithm k-means, clustering and classification of different, it is the unsupervised process,
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Description: c++实现的KNN库:建立高维度的K-d tree,实现K邻域搜索，最小半径搜索-K-NN algorithm implementation.
It supports data structures and algorithms for both exact and approximate nearest neighbor searching in arbitrarily high dimensions.
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Description: k-nn算法实现分类，模式识别作业，分三类，75个训练集，25个测试集，输出对测试集分类的结果-k-nn classification algorithm, pattern recognition operations are classified into three types, 75 training sets, 25 test set, the output of the test set classification results
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Description: Dear all: this is the k-nn algorithm, written in Matlab.
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Description: k-NN classifier in matlab
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Description: k-nearest neighbour classifier source code
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Description: 用matlab写的最近邻和K近邻法分类器，简单易懂，适合初学者-Written with matlab and K-NN nearest neighbor classifier, easy to understand for beginners
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Description: Voice recognition LPC VS MFCC with K--Voice recognition LPC VS MFCC with K-NN
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Description: 以著名的wine数据作为实验样本。包括k-NN算法，交叉验证，PCA降维等。-With the famous wine data as experimental samples.K- NN algorithm, cross validation, PCA dimension reduction, etc.
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Description: 三种分类器：决策树分类器，k-NN分类器和k-means分类器的运行时间以及运行准确率的比较。(Three kinds of classifiers: decision tree classifier, k-NN classifier and K-means classifier running time and accuracy comparison.)
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Description: k-nn算法 K-NN算法 ( K Nearest Neighbor, K近邻算法 ), 是机器学习中的一个经典算法, 比较简单且容易理解. K-NN算法通过计算新数据与训练数据特征值之间的距离, 然后选取 K (K>=1) 个距离最近的邻居进行分类或者回归. 如果K = 1 , 那么新数据将被分配给其近邻的类.(k-nnK - NN algorithm (K on his Neighbor, K Nearest Neighbor algorithm), is a classical algorithm of machine learning, more simple and easy to understand. K - NN algorithm by calculating the new data and the distance between the training data characteristic value, and then select K (K > = 1) from the Nearest Neighbor classification or regression. If K = 1, then the new data will be class assigned to its neighbours.)
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