文件名称:metric-learning_survey_v2
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- 人工智能/神经网络/遗传算法
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- [PDF]
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- 2012-11-26
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- 315kb
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- 刘**
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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
learning conditions: supervised learning versus unsupervised learning, learning in a global sense versus in a local sense and the distance matrix based on linear kernel versus nonlinear kernel. In addition, this paper discusses a number of techniques
that is central to distance metric learning, including convex programming, positive semi-definite programming, kernel learning, dimension reduction, K Nearest Neighbor, large margin classification, and graph-based approaches.
metric learning from a principle perspective, and includes a broad selection of recent work. In particular, distance metric learning is reviewed under different
learning conditions: supervised learning versus unsupervised learning, learning in a global sense versus in a local sense and the distance matrix based on linear kernel versus nonlinear kernel. In addition, this paper discusses a number of techniques
that is central to distance metric learning, including convex programming, positive semi-definite programming, kernel learning, dimension reduction, K Nearest Neighbor, large margin classification, and graph-based approaches.
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metric learning_survey_v2.pdf