Titlebook: ;

Blasphemy

Affectation

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.

LITHE

領(lǐng)巾

惰性氣體

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

Introvert

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

凝視

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

合唱團(tuán)

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

改良

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.

Glower

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