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汇编指令字典2.0对学习汇编语言非常有用-dictionary compilation directives 2.0 pairs learning assembly language is very useful!
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oracle 内部函数，方便学习，函数字典-oracle internal function to facilitate learning, function dictionary
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Usb版BT3用SPOONWPA研究学习WPA更换字典和握手包的方法图解-Usb version of BT3 with SPOONWPA dictionary and learning WPA handshake packet replacement means graphical
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数字图像处理，K-SVD字典学习方法，信号的稀疏与冗余表示理论，图像压缩，图像去噪-Digital image processing, K-SVD dictionary learning methods, sparse and redundant signal representation theory, image compression, image denoising
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KSVD和MOD字典学习稀疏表示程序代码-KSVD, and MOD dictionary learning sparse representation of program code
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用友通数据库字典,供用友使用者学习和二开使用,也可以供ERP系统程序员学习大型ERP系统的数据库设计思路.-UF database dictionary for UF user learning and open use of large ERP system database design ideas for ERP systems programmers learning.
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Dictionary Learning by L1-Minimization.
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燕京大学硕士论文，关于稀疏编码，涉及到多层字典学习算法，很优秀的论文-Yenching University Master' s thesis on sparse coding, involving multi-dictionary learning algorithm, very good papers
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稀疏字典学习的K-SVD法介绍的论文，是算法的理论基础。-Sparse Dictionary Learning K-SVD method presented papers, the theoretical basis of the algorithm.
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针对稀疏表示识别方法需要大量样本训练过完备字典且特征冗余度较高的问题，提出了结合过完备字典学习与PCA降维的小样本语音情感识别算法.该方法首先用PCA降维方法将特征降维，再将处理后的特征用于过完备字典训练与稀疏表示识别方法，从而给出了语音情感特征的稀疏表示方法，并确定了新算法的具体步骤.为验证其有效性，在同等特征维数下，将方法与BP, SVM进行比较，并对比、分析语音情感特征稀疏化前后对语音情感识别率、时间效率以及空间效率的影响.试验结果表明，所提出方法的识别率比SVM与BP高 与采用稀疏化前的
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Analysis K-SVD: A Dictionary-Learning Algorithm for the Analysis Sparse Model.
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K均值，K-SVD算法，字典学习，英文版算法的翻译-K-means, K-SVD algorithm, dictionary learning algorithm in English translation
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Convolutive dictionary learning
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基于学习字典的图像冗余表示，用来进行图像去噪-Image-based learning dictionary redundant representation for image denoising
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基于Gabor特征和字典学习的高斯混合稀疏表示图像识别-Image recognition based on Gabor features and dictionary learning of Gauss hybrid sparse representation
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ksvd字典学习的内容介绍，操作流程，与计算公式-Content ksvd dictionary learning introduction, operational processes, and formulas
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joint sparse representation
(JSR)方法用于车内语音增强的特征降噪算法-address reducing the mismatch between training
and testing conditions for hands-free in-car speech recognition. It
is well known that the distortions caused by background noise,
channel effec
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本文档是Karl Skretting的博士论文，主要研究dictionary learning，以及重叠帧里稀疏信号的呈现。-This document is Karl Skretting s PhD thesis, which focuses on dictionary learning and the presentation of sparse signals in overlapping frames.
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一种基于KSVD字典学习的多任务超分辨率图像重构方法,主要解决现有方法在高放大因子下重构图像质量下降比较严重的问题(A multi task super-resolution image reconstruction method based on KSVD dictionary learning is mainly used to solve the problem that the quality of image reconstructed by the existing method i
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这是一篇关于字典学习的国内最新版综述，详细的描述了不同字典学习模型下的典型算法，对于稀疏表示领域的同学有一定的参考价值和作用。(This is a domestic latest edition of dictionary learning, which describes in detail the typical algorithms under different dictionary learning models. It has a certain reference value an
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