introspective
发表于 2025-3-21 16:09:28
书目名称Graph-Based Representations in Pattern Recognition影响因子(影响力)<br> http://figure.impactfactor.cn/if/?ISSN=BK0388002<br><br> <br><br>书目名称Graph-Based Representations in Pattern Recognition影响因子(影响力)学科排名<br> http://figure.impactfactor.cn/ifr/?ISSN=BK0388002<br><br> <br><br>书目名称Graph-Based Representations in Pattern Recognition网络公开度<br> http://figure.impactfactor.cn/at/?ISSN=BK0388002<br><br> <br><br>书目名称Graph-Based Representations in Pattern Recognition网络公开度学科排名<br> http://figure.impactfactor.cn/atr/?ISSN=BK0388002<br><br> <br><br>书目名称Graph-Based Representations in Pattern Recognition被引频次<br> http://figure.impactfactor.cn/tc/?ISSN=BK0388002<br><br> <br><br>书目名称Graph-Based Representations in Pattern Recognition被引频次学科排名<br> http://figure.impactfactor.cn/tcr/?ISSN=BK0388002<br><br> <br><br>书目名称Graph-Based Representations in Pattern Recognition年度引用<br> http://figure.impactfactor.cn/ii/?ISSN=BK0388002<br><br> <br><br>书目名称Graph-Based Representations in Pattern Recognition年度引用学科排名<br> http://figure.impactfactor.cn/iir/?ISSN=BK0388002<br><br> <br><br>书目名称Graph-Based Representations in Pattern Recognition读者反馈<br> http://figure.impactfactor.cn/5y/?ISSN=BK0388002<br><br> <br><br>书目名称Graph-Based Representations in Pattern Recognition读者反馈学科排名<br> http://figure.impactfactor.cn/5yr/?ISSN=BK0388002<br><br> <br><br>
Debility
发表于 2025-3-21 20:56:21
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ANN
发表于 2025-3-22 03:49:14
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Frisky
发表于 2025-3-22 06:39:35
https://doi.org/10.1057/9781137368683This contribution extends generalized LVQ, generalized relevance LVQ, and robust soft LVQ to the graph domain. The proposed approaches are based on the basic learning graph quantization (.) algorithm using the orbifold framework. Experiments on three data sets show that the proposed approaches outperform . and ..
frenzy
发表于 2025-3-22 11:57:10
Generalized Learning Graph QuantizationThis contribution extends generalized LVQ, generalized relevance LVQ, and robust soft LVQ to the graph domain. The proposed approaches are based on the basic learning graph quantization (.) algorithm using the orbifold framework. Experiments on three data sets show that the proposed approaches outperform . and ..
辩论
发表于 2025-3-22 15:54:00
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辩论
发表于 2025-3-22 17:51:53
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bacteria
发表于 2025-3-22 23:31:25
Dimensionality Reduction for Graph of Words Embeddinge attributes of the graph. While it shows good properties in classification problems, it suffers from high dimensionality and sparsity. These two issues are addressed in this article. Two well-known techniques for dimensionality reduction, kernel principal component analysis (kPCA) and independent c
本能
发表于 2025-3-23 05:11:53
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巫婆
发表于 2025-3-23 06:54:10
Learning Generative Graph Prototypes Using Simplified von Neumann Entropyerms of learning a generative supergraph model from which the new samples can be obtained by an appropriate sampling mechanism. We commence by constructing a probability distribution for the occurrence of nodes and edges over the supergraph. We encode the complexity of the supergraph using the von-N