Location:
Search - jaccard
Search list
Description: 广义Jaccard系数（Tanimoto系数）：是对Jaccard系数的扩展，可以用于文档数据。
Platform: |
Size: 2976 |
Author: Mrs zhang |
Hits:
Description: 广义Jaccard系数（Tanimoto系数）：是对Jaccard系数的扩展，可以用于文档数据。-Generalized Jaccard coefficient (Tanimoto coefficient): Jaccard coefficient of expansion can be used to document data.
Platform: |
Size: 3072 |
Author: Mrs zhang |
Hits:
Description: Jacard similarity implementation
Platform: |
Size: 67584 |
Author: Qu媒 T脿i |
Hits:
Description: C++ 实现从文件中抽取近似字符串,近似的意义是指编辑距离 和 Jaccard 距离小于阈值-C++ select similar strings from file
the similar string is define by ED and Jaccard distance
Platform: |
Size: 3072 |
Author: 李莉 |
Hits:
Description: Jaccard Based String Matching/Searching Algorithm
Platform: |
Size: 4096 |
Author: vvishal |
Hits:
Description: item_cf alg,this alg based on item cf.especillay use jaccard alg.
Platform: |
Size: 6880256 |
Author: 李磊 |
Hits:
Description: 计算两个字符串的k-gram的jaccard系数，是信息检索理论判断两个字符串相似度的应用。-To calculate the jaccard value of the two strings, in terms of the k_gram theory.
Platform: |
Size: 9004032 |
Author: liuxueq |
Hits:
Description: 使用链表技术，实现字符串相似度计算，重复字母重复计算。-Use list technology, the realization of string similarity calculation, repetitive letter is calculated.
Platform: |
Size: 1024 |
Author: 马昌胜 |
Hits:
Description: 基于最大最小值法的模糊相似矩阵求解matlab源码-Fuzzy similar matrix calculating based on Jaccard similarity coefficient
Platform: |
Size: 1024 |
Author: neo |
Hits:
Description: function [jaccardIdx,jaccardDist] = jaccard_coefficient(img_Orig,img_Seg)
Jaccard index and distance co-efficient of segmemted and ground truth- function [jaccardIdx,jaccardDist] = jaccard_coefficient(img_Orig,img_Seg)
Jaccard index and distance co-efficient of segmemted and ground truth
Platform: |
Size: 2048 |
Author: alaa |
Hits:
Description: Agrupaciones graficas euclidean, jaccard & Manhattan Grafica Avanzada 2
Platform: |
Size: 7168 |
Author: roimar90
|
Hits:
Description: The Normalized Compression Distance (NCD) has been used in
a number of domains to compare objects with varying feature
types. This exibility comes from the use of general purpose compression algorithms as the means of computing distances between
byte sequences. Such exibility makes NCD particularly attractive
for cases where the right features to use are not obvious, such as
malware classication. However, NCD can be computationally demanding, thereby restricting the scale at which it can be applied.
We introduce an alternative metric also inspired by compression,
the Lempel-Ziv Jaccard Distance (LZJD). We show that this new
distance has desirable theoretical properties, as well as comparable
or superior performance for malware classication, while being
easy to implement and orders of magnitude faster in practice
Platform: |
Size: 600363 |
Author: pavlest |
Hits: