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
作者: Debility 時(shí)間: 2025-3-21 20:56 作者: ANN 時(shí)間: 2025-3-22 03:49 作者: Frisky 時(shí)間: 2025-3-22 06:39
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 ..作者: frenzy 時(shí)間: 2025-3-22 11:57
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 ..作者: 辯論 時(shí)間: 2025-3-22 15:54 作者: 辯論 時(shí)間: 2025-3-22 17:51 作者: bacteria 時(shí)間: 2025-3-22 23:31
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作者: 本能 時(shí)間: 2025-3-23 05:11 作者: 巫婆 時(shí)間: 2025-3-23 06:54
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作者: Blasphemy 時(shí)間: 2025-3-23 13:45 作者: Affectation 時(shí)間: 2025-3-23 14:21
Maximum Likelihood for Gaussians on Graphs this observation, we derive a maximum likelihood method for estimating the parameters of the Gaussian on graphs. In conjunction with a naive Bayes classifier, we applied the proposed approach to image classification.作者: LITHE 時(shí)間: 2025-3-23 19:42 作者: 領(lǐng)巾 時(shí)間: 2025-3-24 00:22 作者: 惰性氣體 時(shí)間: 2025-3-24 02:54
Aggregated Search in Graph Databases: Preliminary Resultscostly since it involves subgraph isomorphism testing, which is an NP-complete problem [7]. Most of the existing query processing techniques are based on the framework of filtering-and-verification to reduce computation costs. However, to the best f our knowledge, the problem of assembling graphs to作者: Introvert 時(shí)間: 2025-3-24 06:46
Speeding Up Graph Edit Distance Computation through Fast Bipartite Matchingpresentations is that the computation of various graph similarity measures is exponential in the number of involved nodes. Hence, such computations are feasible for rather small graphs only. One of the most flexible graph similarity measures is graph edit distance. In this paper we propose a novel a作者: 凝視 時(shí)間: 2025-3-24 14:34
Two New Graph Kernels and Applications to Chemoinformatics science’s research fields mainly concerned by the chemoinformatics field are machine learning and graph theory. From this point of view, graph kernels provide a nice framework combining machine learning techniques with graph theory. Such kernels prove their efficiency on several chemoinformatics pr作者: 合唱團(tuán) 時(shí)間: 2025-3-24 17:22
Parallel Graduated Assignment Algorithm for Multiple Graph Matching Based on a Common Labelling algorithm is to perform multiple graph matching in a current desktop computer, but, instead of executing the code in the generic processor, we execute a parallel code in the graphic processor unit. Our new algorithm is ready to take advantage of incoming desktop computers capabilities. While compar作者: 改良 時(shí)間: 2025-3-24 20:58
Smooth Simultaneous Structural Graph Matching and Point-Set Registrationhe graph matching problem as one of mixture modelling which is solved using the EM algorithm. The solution is then approximated as a succession of assignment problems which are solved, in a smooth way, using Softassign. Our method allows us to detect outliers in both graphs involved in the matching.作者: Glower 時(shí)間: 2025-3-25 01:24
Automatic Learning of Edit Costs Based on Interactive and Adaptive Graph Recognitiones of edit costs for deleting and inserting nodes and vertices are crucial to obtain good results in the recognition ratio. Nevertheless, these parameters are difficult to be estimated and they are usually set by a na?ve trial and error method. Moreover, we wish to seek these costs such that the sys作者: ZEST 時(shí)間: 2025-3-25 06:48 作者: 悅耳 時(shí)間: 2025-3-25 08:31 作者: 周年紀(jì)念日 時(shí)間: 2025-3-25 12:40
Indexing with Well-Founded Total Order for Faster Subgraph Isomorphism Detection graphs where the nodes are labeled with a rather small amount of different classes. In order to reduce the number of possible permutations, a weight function for labeled graphs is introduced and a well-founded total order is defined on the weights of the labels. Inversions which violate the order a作者: CRUC 時(shí)間: 2025-3-25 17:18
https://doi.org/10.1007/978-3-211-79194-3stitutes an ingredient of Size Theory, a geometrical/topological approach to shape analysis and comparison. A global method for reducing size graphs is presented, together with a theorem stating that size graphs reduced in such a way preserve all the information in terms of multidimensional size fun作者: 多產(chǎn)魚 時(shí)間: 2025-3-25 23:07
Modern American Reading Practicesbeing invariant under graph isomorphism, are a rich source of information about graph structure. We explore this representation and propose several new graph characteristics that can be used for efficient graph comparison. Experiments on clusterization and classification with synthetic and real-worl作者: 歌唱隊(duì) 時(shí)間: 2025-3-26 03:57 作者: 疲勞 時(shí)間: 2025-3-26 05:19 作者: 新娘 時(shí)間: 2025-3-26 12:18 作者: spondylosis 時(shí)間: 2025-3-26 15:45 作者: 固執(zhí)點(diǎn)好 時(shí)間: 2025-3-26 20:06 作者: 健談的人 時(shí)間: 2025-3-26 22:35 作者: prick-test 時(shí)間: 2025-3-27 04:07 作者: 蒙太奇 時(shí)間: 2025-3-27 05:34
Modern Approaches to Wettabilitycostly since it involves subgraph isomorphism testing, which is an NP-complete problem [7]. Most of the existing query processing techniques are based on the framework of filtering-and-verification to reduce computation costs. However, to the best f our knowledge, the problem of assembling graphs to作者: ENNUI 時(shí)間: 2025-3-27 13:31 作者: audiologist 時(shí)間: 2025-3-27 15:48 作者: laceration 時(shí)間: 2025-3-27 18:36
https://doi.org/10.1007/978-1-4842-6267-2 algorithm is to perform multiple graph matching in a current desktop computer, but, instead of executing the code in the generic processor, we execute a parallel code in the graphic processor unit. Our new algorithm is ready to take advantage of incoming desktop computers capabilities. While compar作者: Electrolysis 時(shí)間: 2025-3-27 23:54
Ronald H. Ottewill,Adrian R. Renniehe graph matching problem as one of mixture modelling which is solved using the EM algorithm. The solution is then approximated as a succession of assignment problems which are solved, in a smooth way, using Softassign. Our method allows us to detect outliers in both graphs involved in the matching.作者: CHART 時(shí)間: 2025-3-28 04:36 作者: innovation 時(shí)間: 2025-3-28 10:19 作者: 把…比做 時(shí)間: 2025-3-28 11:21
Modern Aspects of Electrochemistryrecovery missing data based on dot product representation of graph (DPRG). We commence by building an association graph using the nodes in graphs with high matching probabilities, and treat the correspondences between unmatched nodes as missing data in association graph. Then, we recover corresponde作者: 恃強(qiáng)凌弱 時(shí)間: 2025-3-28 17:16 作者: Hallowed 時(shí)間: 2025-3-28 19:26
Graph-Based Representations in Pattern Recognition作者: 誤傳 時(shí)間: 2025-3-29 01:18
Speeding Up Graph Edit Distance Computation through Fast Bipartite Matchingrse graph representations we demonstrate a high speed up of our proposed method over a traditional algorithm for graph edit distance computation and over two other sub-optimal approaches that use the Hungarian and Munkres algorithm. Also, we show that classification accuracy remains nearly unaffecte作者: Digitalis 時(shí)間: 2025-3-29 03:32
https://doi.org/10.1007/978-1-4615-0761-1rse graph representations we demonstrate a high speed up of our proposed method over a traditional algorithm for graph edit distance computation and over two other sub-optimal approaches that use the Hungarian and Munkres algorithm. Also, we show that classification accuracy remains nearly unaffecte作者: Lime石灰 時(shí)間: 2025-3-29 07:26 作者: Meditate 時(shí)間: 2025-3-29 13:46 作者: 暫時(shí)過來 時(shí)間: 2025-3-29 16:51 作者: bacteria 時(shí)間: 2025-3-29 23:01 作者: 祖?zhèn)髫?cái)產(chǎn) 時(shí)間: 2025-3-30 00:23 作者: 6Applepolish 時(shí)間: 2025-3-30 05:06 作者: Decimate 時(shí)間: 2025-3-30 11:42 作者: Seminar 時(shí)間: 2025-3-30 12:24
https://doi.org/10.1007/978-3-031-24086-7d in the literature. Secondly, we discuss, empirically, the choice of a graph-based representation on three different image databases and show that the representation has a real impact on the method performances and experimental results in the literature on graph performance evaluation for similarity measures should be considered carefully.作者: GEM 時(shí)間: 2025-3-30 18:29 作者: VOC 時(shí)間: 2025-3-30 21:05
Antoni S. Folkers,Belinda A. C. van Buitenoblems. This paper presents two new graph kernels applied to regression and classification problems within the chemoinformatics field. The first kernel is based on the notion of edit distance while the second is based on sub trees enumeration. Several experiments show the complementary of both approaches.作者: Abrade 時(shí)間: 2025-3-31 02:30 作者: 鎮(zhèn)壓 時(shí)間: 2025-3-31 07:11 作者: 土坯 時(shí)間: 2025-3-31 09:16 作者: 結(jié)合 時(shí)間: 2025-3-31 16:09 作者: fidelity 時(shí)間: 2025-3-31 21:07 作者: 生存環(huán)境 時(shí)間: 2025-4-1 00:21 作者: 打算 時(shí)間: 2025-4-1 03:30 作者: 徹底明白 時(shí)間: 2025-4-1 08:48
Exploration of the Labelling Space Given Graph Edit Distance Costs and we show that its minimization lead to a few different labellings and so, most of the labellings in the labelling space cannot be obtained. Moreover, we present a method that using some of the new properties of the Graph Edit Distance speeds up the computation of all possible labellings.作者: 執(zhí)拗 時(shí)間: 2025-4-1 11:15
