首页> 外文OA文献 >An effective biometric discretization approach to extract highly discriminative, informative, and privacy-protective binary representation

【2h】

An effective biometric discretization approach to extract highly discriminative, informative, and privacy-protective binary representation

机译：一种有效的生物特征离散化方法，可提取具有高度区分性，信息性和隐私保护性的二进制表示形式

代理获取

本网站仅为用户提供外文OA文献查询和代理获取服务，本网站没有原文。下单后我们将采用程序或人工为您竭诚获取高质量的原文，但由于OA文献来源多样且变更频繁，仍可能出现获取不到、文献不完整或与标题不符等情况，如果获取不到我们将提供退款服务。请知悉。

页面导航

著录项
相似文献
相关主题

著录项

作者
Meng-Hui Lim; Andrew Beng Jin Teoh;
展开▼
作者单位

展开▼
年度 2012
总页数
原文格式 PDF
正文语种
中图分类

相似文献

外文文献
中文文献
专利

1. An effective biometric discretization approach to extract highly discriminative, informative, and privacy-protective binary representation [J] . Lim Meng-Hui, Teoh Andrew EURASIP journal on advances in signal processing . 2011,第20aPta3期

机译：一种有效的生物特征离散化方法，可提取具有高度区分性，信息性和隐私保护性的二进制表示形式
2. An effective biometric discretization approach to extract highly discriminative, informative, and privacy-protective binary representation [J] . Meng-Hui Lim, Andrew Beng Jin Teoh EURASIP journal on advances in signal processing . 2011,第1期

机译：一种有效的生物特征离散化方法，可提取具有高度歧视性，信息性和隐私保护性的二进制表示形式
3. A Semi-nonparametric Approach to Joint Modeling of A Primary Binary Outcome and Longitudinal Data Measured at Discrete Informative Times [J] . Song Yan, Daowen Zhang, Wenbin Lu, Statistics in Biosciences . 2012,第2期

机译：半非参数方法对离散信息时间测得的主要二进制结果和纵向数据进行联合建模
4. Geo-informative discriminative image representation by semi-supervised hierarchical topic modeling [C] . Li Zijian, Tang Siliang, Shao Jian, IEEE International Conference on Multimedia and Expo . 2014

机译：半监督分层主题建模的地理信息判别性图像表示
5. Learning Effective Binary Representation with Deep Hashing Technique for Large-Scale Multimedia Similarity Search [D] . Wu, Gengshen. 2020

机译：学习具有深度散列技术的有效二进制表示，用于大规模多媒体相似性搜索
6. A Seminonparametric Approach to Joint Modeling of A Primary Binary Outcome and Longitudinal Data Measured at Discrete Informative Times [O] . Song Yan, Daowen Zhang, Wenbin Lu, -1

机译：在离散信息时间测量的主要二元结果和纵向数据的联合建模的SeminonAmetric方法
7. Figure 6: (A) Tree resulted the standard MP analysis of the 98-loci supermatrix of the Great Apes (Hominidae, Primates, Mammalia) constructed using the modified summary of Lehtonen et al. (2011) (see “Materials and Methods” for the details) and a posteriori rooted relatively Papio (length = 12,092, CI = 0.7877, RI = 0.5597); 34,022 characters are constant, and the number of the parsimony-informative characters is equal to 4,311. MP BS values are provided below branches. The only characters with non-ambiguous value of the outgroup (Papio) have been saved within concatenated alignment; (B) the average consensus tree of the score 0.00084 resulted the analysis of the Hennigian forest of the 5,507 trees derived from the binary representation of the 98-loci supermatrix of the Great Apes (Hominidae, Primates, Mammalia) (see “Materials and Methods” for the details); Papio is assumed to be the best all-plesiomorphic group. “Additional…” FORESTER’s output tree file (see “Materials and Methods” for the details) selected for future analyses; (C) the average consensus tree of the score 0.00090 resulted the analysis of the Hennigian forest of the 5,339 trees derived from the modified binary representation of the 98-loci supermatrix of the Great Apes (Hominidae, Primates, Mammalia) (see 6.I. and “Materials and Methods” for the details), but with all 168 trees, which contain the clade (Homo plus Pan) have been removed from the input forest. “Additional…” FORESTER’s output tree file (see “Materials and Methods”) selected for future analyses. [O] . -1

机译：图6：（A）树导致的标准MP分析的98-基因座的类人猿（人科，灵长类动物，哺乳动物），使用莱赫托宁等人的修改的摘要构成的超级矩阵。（2011）（参见细节“材料和方法”）以及相对植根狒狒后验概率（长度= 12092，CI = 0.7877，RI = 0.5597）; 34022个字符是恒定的，并且简约信息的字符数等于4311。下面提供分支MP BS值。与外类群（狒狒）的非模糊值的唯一的字符已被保存的级联对准内; （B）的得分0.00084的平均一致树导致从巨猿（原始人类，灵长类动物，哺乳动物）的98座超级矩阵的二进制表示获得的5507棵的Hennigian森林的分析（见“材料和方法”为细节）;狒狒被认为是最好的全祖征群。 “其他...” FORESTER的输出树文件（请参阅详细信息“材料和方法”），用于进一步的分析; （C）的分数0.00090的平均一致树导致从98个位点的类人猿（人科，灵长类动物，哺乳动物）的超级矩阵（见6.I的改性二进制表示导出的5339种树木的Hennigian森林的分析以及“材料和方法”的详细说明），但所有168棵，其中包含的分支（智人加PAN）已经从输入林中删除。 “其他...” FORESTER的输出树文件（参见“材料和方法”），用于进一步的分析。

An effective biometric discretization approach to extract highly discriminative, informative, and privacy-protective binary representation

著录项

相似文献

相关主题

期刊订阅