Titlebook: ;

probiotic

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)學科排名




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




書目名稱Graph-Based Representations in Pattern Recognition網(wǎng)絡公開度學科排名




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讀者反饋學科排名

不可思議

An Entropic Edge Assortativity Measureapply our novel assortativity characterization to both artificial random graphs and real-world networks. The experimental results demonstrate that our measure is effective in characterizing the structural complexity of networks and classifying networks that belong to different complexity classes.

泥沼

Coupled-Feature Hypergraph Representation for Feature Selectionnew data representation, we use a new information theoretic criterion referred to as multivariate mutual information to measure the high-order feature combinations with respect to the class labels. Therefore, we construct a coupled feature hypergraph to model the high-order relations among features.

可用

Organization

Incremental Embedding Within a Dissimilarity-Based Framework point somehow violates the usual separation between training and test sets since both sets should be jointly processed and is an important limitation in many practical applications where the test set is unbounded and unknown during the learning phase. Moreover, requiring the whole universe represen

analogous

Consensus of Two Graph Correspondences Through a Generalisation of the Bipartite Graph Matchingon and also the graph matching could be reduced. We present a consensus method which, given two correspondences between two pairs of attributed graphs generated by separate entities and with different attribute domains, enounces a final correspondence consensus considering the existence of outliers.

analogous

A Hypergraph Matching Framework for Refining Multi-source Feature Correspondences of the hypergraph through higher order clustering. Our method is invariant to scale variation of objects because of its capability for characterizing higher order structure. Furthermore, our method is computationally more efficient than existing hypergraph matching methods because the feature match

fledged

VF2 Plus: An Improved version of VF2 for Biological Graphs2 algorithm that enable it to compete with more recently proposed graph matching techniques. Finally, we evaluate the effectiveness of these enhancement by comparing the matching performance both with the original VF2 and with several recent algorithms, using both the widely known MIVIA graph databa

Agnosia

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