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the baysian distance metric learning code
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Watermarking embeds information into a digital signal like
audio, image, or video. Reversible image watermarking can restore the
original image without any distortion after the hidden data is extracted.
In this paper, we present a novel reversi
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关于metric learning的综述，涉及到许多的知识：SVM、kernel、SDP等-This paper surveys the field of distance
metric learning from a principle perspective, and includes a broad selection of recent work. In particular, distance metric learning is reviewed under different
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基于L1范数稀疏距离测度学习的单类分类算法Based on the L1 norm of sparse distance metric learning one class classification algorithm-Based on the L1 norm of sparse distance metric learning one class classification algorithm
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Semi-Supervised Distance Metric Learning for Collaborative Image Retrieval
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5.采用概率密度比值估计的距离度量学习.rar-5.Probability density ratio is estimated using a distance metric learning.rar
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关于度量学习的一篇经典文章，利用度量学习提高KNN的分类正确率，如果对度量学习有兴趣，这篇是必读的文章-Metric learning about a classic article, the use of metrics to improve learning KNN classification accuracy, if you are interested to measure learning, this is a must read article
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SOM神经网路和DML的结合，CVPR2014文章仿真，保证分类器的Discriminative特性和SOM快速匹配特性！-the combination of the SOM neural network and the distance metric learning method
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大间隔最近邻居（Large margin nearest neighbor (LMNN)）分类算法是统计学的一种机器学习算法。该算法是在k近邻分类其中学习一种欧式距离度量函数。-Spaced nearest neighbor (Large margin nearest neighbor (LMNN)) classification algorithm is a statistical machine learning algorithms. The algorithm is learning a
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三种距离侧度算法，KISSME，XQDA，LSSL-Three kinds of distance measure algorithm, KISS ME, XQDA, LSSL
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基于马氏距离度量的局部线性嵌入算法
局部线性嵌入算法(LLE)中常用欧氏距离度量样本间相似度.而 对于图像等高维数据,欧氏距离不能准确体现样本间的相似程度.文中提出基于马氏距离度量的局部线性嵌入算法(MLLE).算法首先从现有样本中学习到一个 马氏度量,然后在LLE算法的近邻选择、现有样本及新样本降维过程中用马氏度量作为相似性度量.将MLLE算法及其它典型的流形学习算法在ORL和 USPS数据库上进行对比实验,结果表明MLLE算法具有良好的识别性能.
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