Description: 对模式识别中的字符串模板匹配问题进行的研究，设计了对任意字符串进行匹配和字符串相似度，即列文斯顿距离进行计算的算法-right pattern recognition template matching string for the research and design of the arbitrary strings and string matching similarity, Ken Livingstone distance calculation algorithm Platform: |
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Author:蓝色梦幻 |
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Description: 聚类算法大部分都使用距离来计算相似度，本文探讨了无需使用距离的方法。-clustering algorithm used to calculate similarity distance, the paper discusses the need to use the distance. Platform: |
Size: 32883 |
Author:石支柱 |
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Description: 对模式识别中的字符串模板匹配问题进行的研究，设计了对任意字符串进行匹配和字符串相似度，即列文斯顿距离进行计算的算法-right pattern recognition template matching string for the research and design of the arbitrary strings and string matching similarity, Ken Livingstone distance calculation algorithm Platform: |
Size: 2048 |
Author:蓝色梦幻 |
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Description: 聚类算法大部分都使用距离来计算相似度，本文探讨了无需使用距离的方法。-clustering algorithm used to calculate similarity distance, the paper discusses the need to use the distance. Platform: |
Size: 32768 |
Author:石支柱 |
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Description: 文件相似度比较器：
1。 采用标准的 Levenshtein Distance 算法计算两个文件的相似度。
2。 程序使用简单。选入多个文件，然后按分析便个分析出两个文件之间的相似程度。
3。 发现问题欢迎和我讨论。 E-mail: hackerqc@gmail.com-document similarity comparison : 1. Standard Levenshtein Distance algorithm similarity of the two documents. 2. The use of simple procedures. Elected to multiple files, then analyzes analysis of the two documents between the degree of similarity. 3. Discover and I welcome discussion. E-mail : hackerqc@gmail.com Platform: |
Size: 434176 |
Author:覃超 |
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Description: 一种高效的聚类算法给定要聚类的N的对象以及N*N的距离矩阵(或者是相似性矩阵), 层次式聚类方法的基本步骤(参看S.C. Johnson in 1967)如下:-An Efficient Algorithm for the cluster must be the object of N and N* N distance matrix (or similarity matrix), the hierarchical clustering method the basic steps (see S. C. Johnson in 1967), as follows : Platform: |
Size: 432128 |
Author:毛显锋 |
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Description: The Matlab functions and scripts in the MA toolbox are:
- ma_sone wav (PCM) to sone (specific loudness sensation)
- ma_mfcc wav (PCM) to MFCCs (Mel Frequency Cepstrum Coefficients)
- ma_sh sone to Spectrum Histogram
- ma_ph sone to Periodicity Histogram
- ma_fp sone to Fluctuation Pattern
- ma_fc frame based representation (MFCCs or sone) to cluster model (Frame Clustering)
- ma_cms cluster models to distance (Cluster Model Similarity)
- ma_kmeans kmeans clustering (used by "ma_fc")
- ma_cm_visu visualize a cluster model (as returned by "ma_fc")
- ma_simple_eval script for a simple evaluation of similarity measures
- ma_simple_iom script for a simple islands of music interface
-The Matlab functions and scripts in the MA t oolbox are :- ma_sone wav (PCM) to betamethasone ('s specific loudness ensation)- ma_mfcc wav (PCM) to MFCCs (Mel Freq uency diagnoses Coefficients)- ma_sh betamethasone to Sp ectrum Histogram- ma_ph betamethasone to Periodicity Hi stogram- ma_fp betamethasone to Fluctuation Pattern-ma _fc frame based representation (MFCCs or betamethasone) to cluster model (Frame Clustering)- ma_cms cl uster models to distance (Cluster Model Simila rity)- ma_kmeans kmeans clustering (used by "m a_fc ")- ma_cm_visu visualize a cluster model ( as returned by "ma_fc")- ma_simple_eval scrip not for a simple evaluation of similarity measure s-ma_simple_iom script for a simple islands of music interface Platform: |
Size: 24576 |
Author:mesu |
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Description: 欧几里德距离计算代码，通常用于特征相似性度量-Euclidean distance-based code, normally used for the characteristics of similarity measure Platform: |
Size: 16384 |
Author:建国 |
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Description: String similarity , improve string similarity capture by applying shortest distance between words. This source code is implemented in C#. Platform: |
Size: 11264 |
Author:thanh dao |
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Description: 一种基于方向信息的鲁棒型Hausdorff距离匹配方法。该方法采用方向信息提取图
像边缘,通过计算边缘匹配率( edge matching rate, EMR)获得候选匹配区域,然后采用修正后的Hausdorff距离构造
相似性测度。实验结果表明,该方法加快了匹配过程,提高了抗噪性能,并能够准确匹配含有遮挡和伪边缘点的图
像,从而解决了基于传统Hausdorff距离匹配方法因噪声点、伪边缘点和出格点而造成的误匹配问题。-Based on the direction of information Robust Hausdorff distance matching method. Methods The direction of the image edge information extraction, matching the rate by calculating the edge (edge matching rate, EMR) access to the candidate matching area, and then adopt a revised structure Hausdorff distance similarity measure. Experimental results show that the method to accelerate the matching process and enhance the anti-noise performance and the ability to accurately match the containing block and pseudo-edge image, so as to solve the Hausdorff distance based on the traditional matching method because of the noise, the pseudo-edge point and the far point matching problem caused by misuse. Platform: |
Size: 1140736 |
Author:南飞燕 |
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Description: 用的是局部颜色特征，再说细点是用里面的区域颜色直方图的方法。把图像归一化到256X256，把图像分成4X4块，计算16个区域的颜色直方图、、、
最后计算相似度是用欧氏距离.-Using local color feature, repeat fine-point is inside the regional color histogram method. The normalized image to 256X256, the image is divided into 4X4 blocks, the calculation of the 16 regional color histogram, calculated similarity is Euclidean distance. Platform: |
Size: 12361728 |
Author:周文 |
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Description: 采用编辑距离计算词语相似度方法程序，可以嵌入其它程序-Edit the word using a distance-based similarity method of procedure, can be embedded in other programs Platform: |
Size: 1024 |
Author:刘翔 |
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Description: k-means 算法的工作过程说明如下：首先从n个数据对象任意选择 k 个对象作为初始聚类中心；而对于所剩下其它对象，则根据它们与这些聚类中心的相似度（距离），分别将它们分配给与其最相似的（聚类中心所代表的）聚类；然后再计算每个所获新聚类的聚类中心（该聚类中所有对象的均值）；不断重复这一过程直到标准测度函数开始收敛为止。-k-means algorithm process as follows: First of all, the object data from the n choose k object as initial cluster centers and the remaining for the other object, then according to their cluster center with those of the similarity (distance) respectively assigned to them with the most similar (represented by cluster center) clustering obtained and then calculated for each new cluster center clustering (all objects in the cluster mean) repeated this process until the standard measure of function until the beginning of convergence. Platform: |
Size: 1024 |
Author:lining |
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Description: 最大公共子串和字符编辑距离算法的类实现。
函数第一个参数是用来比较的字符串，
第二个参数是被比较的字符串。
lCommonStr第三个参数是前两个字符串的公共子串。-The largest common sub-string and the character edit distance algorithm to achieve the category. Function of the first parameter is used to compare the string, the second parameter is the string to be compared. lCommonStr third parameter is the string the first two public substring. Platform: |
Size: 2048 |
Author:尤凤凯 |
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Description: 小波变换在信号处理、图像处理、模式识别、计算机视觉等
方面广泛应用。本文提出了一种基于Haar小波的时问序列相似
模式匹配模型．它首先对时间序列数据进行Haar小波变换．以
降低数据的维度。首先将所要查询的时间序列进行降维，也就是
对时间序列数据进行小波变换，对序列进行标准化131，得到降维
之后的小波序列．这样可以得到系数子集的良好近似采用尺度
序列表示原始序列．并将其看成多维空间中的一个点，通过计算
两序列差的平方和的平方根作为这两个时间序列的距离函效
(即欧氏距离)。用小波变换保持了局部性质。而欧氏距离就是要
判断相似性的距离函数．所以当在作完变换之后。通过低频分量
来压缩数据，成功的计算出实际距离的下界。如果计算的结果小
于一个由用户所定义的门槛值．则认为这两个时问序列是相似
的。
在-Of wavelet transform in signal processing, image processing, pattern recognition, computer vision, etc.
Aspects of wider application. In this paper, Haar wavelet-based time series similarity
Pattern matching model. It is first time-series data, Haar wavelet transform. To
To reduce the data dimension. Will first want to check the time series dimension reduction, that is,
Pairs of time series data using wavelet transform, standardize the sequence 131, to be reduced-
After the wavelet sequence. This can be a good approximation coefficients of a subset of the use of scale
Sequence indicated that the original sequence. And to treat it as a multi-dimensional space a point, by calculating the
Two sequences and the square root of the square of the difference as the distance between these two time series letter effect
(Ie, Euclidean distance). Maintained with the local nature of the wavelet transform. The Euclidean distance is to
To determine simila Platform: |
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Author:lx |
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Description: 这个包里包含了四种常用的时间序列相似性评估算法，包括DTW，LCSS, PLA和PAA，方便学者在进行开展实验时进行对比分析，也可以运用在多种需要评估距离的场景中。(This package includes four common time series similarity measures, including DTW, LCSS,PLA and PAA. This package can be used when the researcher carries out experiments on time series measures, and they can also be applied in different situation that needs the distance measure.) Platform: |
Size: 1024 |
Author:heavenyyq |
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Search - similarity distance