Publisher: World Scientific Pub Co Inc (September 3, 2007)
Format: PDF / Kindle / ePub
Size: 6.1 MB
Downloadable formats: PDF
In graph-based structural trend attractiveness, the belief is to remodel styles into graphs and practice the research and popularity of styles within the graph area - normally known as graph matching. a number of equipment for graph matching were proposed. Graph edit distance, for example, defines the dissimilarity of 2 graphs by way of the volume of distortion that's had to rework one graph into the opposite and is taken into account probably the most versatile equipment for error-tolerant graph matching.This ebook makes a speciality of graph kernel features which are hugely tolerant in the direction of structural error. the fundamental thought is to include techniques from graph edit distance into kernel features, therefore combining the flexibleness of edit distance-based graph matching with the facility of kernel machines for development reputation. The authors introduce a set of novel graph kernels on the topic of edit distance, together with diffusion kernels, convolution kernels, and random stroll kernels. From an experimental assessment of a semi-artificial line drawing info set and 4 real-world info units such as images, microscopic pictures, fingerprints, and molecules, the authors show that many of the kernel features along side aid vector machines considerably outperform conventional edit distance-based nearest-neighbor classifiers, either by way of category accuracy and operating time.
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