Deutsch
 
Hilfe Datenschutzhinweis Impressum
  DetailsucheBrowse

Datensatz

DATENSATZ AKTIONENEXPORT

Freigegeben

Konferenzbeitrag

A Regularization Framework for Learning from Graph Data

MPG-Autoren
/persons/resource/persons84330

Zhou,  D
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

/persons/resource/persons84193

Schölkopf,  B
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

Externe Ressourcen
Volltexte (beschränkter Zugriff)
Für Ihren IP-Bereich sind aktuell keine Volltexte freigegeben.
Volltexte (frei zugänglich)

Zhou-Schoelkopf-2004.pdf
(beliebiger Volltext), 204KB

Ergänzendes Material (frei zugänglich)
Es sind keine frei zugänglichen Ergänzenden Materialien verfügbar
Zitation

Zhou, D., & Schölkopf, B. (2004). A Regularization Framework for Learning from Graph Data. In ICML 2004 Workshop on Statistical Relational Learning and Its Connections to Other Fields (SRL 2004) (pp. 132-137).


Zitierlink: https://hdl.handle.net/11858/00-001M-0000-0013-F3AB-E
Zusammenfassung
The data in many real-world problems can be thought of as a graph, such as the web, co-author networks, and biological networks. We propose a general regularization framework on graphs, which is applicable to the classification, ranking, and link prediction
problems. We also show that the method can be explained as lazy
random walks. We evaluate the method on a number of experiments.