Titlebook: ;

introspective

書目名稱Graph-Based Representations in Pattern Recognition影響因子(影響力)




書目名稱Graph-Based Representations in Pattern Recognition影響因子(影響力)學(xué)科排名




書目名稱Graph-Based Representations in Pattern Recognition網(wǎng)絡(luò)公開度




書目名稱Graph-Based Representations in Pattern Recognition網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Graph-Based Representations in Pattern Recognition被引頻次




書目名稱Graph-Based Representations in Pattern Recognition被引頻次學(xué)科排名




書目名稱Graph-Based Representations in Pattern Recognition年度引用




書目名稱Graph-Based Representations in Pattern Recognition年度引用學(xué)科排名




書目名稱Graph-Based Representations in Pattern Recognition讀者反饋




書目名稱Graph-Based Representations in Pattern Recognition讀者反饋學(xué)科排名

Debility

ANN

Frisky

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

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 ..

辯論

辯論

bacteria

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

巫婆

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