Abstract
he recent development of graph kernel functions has made it possible to apply well-established machine learning methods to graphs. However, to allow for analyses that yield a graph as a result, it is necessary to solve the so-called pre-image problem: to reconstruct a graph from its feature space representation induced by the kernel. Here, we suggest a practical solution to this problem. © Springer-Verlag 2004.
Cite
CITATION STYLE
Bakir, G. H., Zien, A., & Tsuda, K. (2004). Learning to find graph pre-images. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3175, 253–261. https://doi.org/10.1007/978-3-540-28649-3_31
Register to see more suggestions
Mendeley helps you to discover research relevant for your work.
