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- Graph_kernel abstract "In structure mining, a domain of learning on structured data objects in machine learning, a graph kernel is a kernel function that computes an inner product on graphs. Graph kernels can be intuitively understood as functions measuring the similarity of pairs of graphs. They allow kernelized learning algorithms such as support vector machines to work directly on graphs, without having to do feature extraction to transform them to fixed-length, real-valued feature vectors. They find applications in bioinformatics, in chemoinformatics (as a type of molecule kernels), and in social network analysis.Graph kernels were first described in 2002 by R. I. Kondor and John Laffertyas kernels on graphs, i.e. similarity functions between the nodes of a single graph, with the World Wide Web hyperlink graph as a suggested application. Vishwanathan et al. instead defined kernels between graphs.An example of a kernel between graphs is the random walk kernel, which conceptually performs random walks on two graphs simultaneously, then counts the number of paths that were produced by both walks. This is equivalent to doing random walks on the direct product of the pair of graphs, and from this, a kernel can be derived that can be efficiently computed.".
- Graph_kernel wikiPageID "39419087".
- Graph_kernel wikiPageLength "2896".
- Graph_kernel wikiPageOutDegree "22".
- Graph_kernel wikiPageRevisionID "649104467".
- Graph_kernel wikiPageWikiLink Bioinformatics.
- Graph_kernel wikiPageWikiLink Category:Graph_algorithms.
- Graph_kernel wikiPageWikiLink Category:Kernel_methods_for_machine_learning.
- Graph_kernel wikiPageWikiLink Cheminformatics.
- Graph_kernel wikiPageWikiLink Feature_extraction.
- Graph_kernel wikiPageWikiLink Feature_vector.
- Graph_kernel wikiPageWikiLink Graph_(abstract_data_type).
- Graph_kernel wikiPageWikiLink Hyperlink.
- Graph_kernel wikiPageWikiLink Inner_product_space.
- Graph_kernel wikiPageWikiLink Kernel_method.
- Graph_kernel wikiPageWikiLink Machine_learning.
- Graph_kernel wikiPageWikiLink Molecule_mining.
- Graph_kernel wikiPageWikiLink Path_(graph_theory).
- Graph_kernel wikiPageWikiLink Positive-definite_kernel.
- Graph_kernel wikiPageWikiLink Random_walk.
- Graph_kernel wikiPageWikiLink Social_network_analysis.
- Graph_kernel wikiPageWikiLink Structure_mining.
- Graph_kernel wikiPageWikiLink Support_vector_machine.
- Graph_kernel wikiPageWikiLink Tensor_product_of_graphs.
- Graph_kernel wikiPageWikiLink Tree_kernel.
- Graph_kernel wikiPageWikiLink World_Wide_Web.
- Graph_kernel wikiPageWikiLinkText "Diffusion Kernel".
- Graph_kernel wikiPageWikiLinkText "Graph kernel".
- Graph_kernel wikiPageWikiLinkText "graph kernel".
- Graph_kernel wikiPageWikiLinkText "graphs".
- Graph_kernel wikiPageUsesTemplate Template:About.
- Graph_kernel wikiPageUsesTemplate Template:Comp-sci-stub.
- Graph_kernel wikiPageUsesTemplate Template:Reflist.
- Graph_kernel subject Category:Graph_algorithms.
- Graph_kernel subject Category:Kernel_methods_for_machine_learning.
- Graph_kernel hypernym Function.
- Graph_kernel type Disease.
- Graph_kernel comment "In structure mining, a domain of learning on structured data objects in machine learning, a graph kernel is a kernel function that computes an inner product on graphs. Graph kernels can be intuitively understood as functions measuring the similarity of pairs of graphs. They allow kernelized learning algorithms such as support vector machines to work directly on graphs, without having to do feature extraction to transform them to fixed-length, real-valued feature vectors.".
- Graph_kernel label "Graph kernel".
- Graph_kernel sameAs Q17018775.
- Graph_kernel sameAs m.0vpw6gh.
- Graph_kernel sameAs Q17018775.
- Graph_kernel wasDerivedFrom Graph_kernel?oldid=649104467.
- Graph_kernel isPrimaryTopicOf Graph_kernel.