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Hier finden Sie wissenschaftliche Publikationen aus den FraunhoferInstituten. Efficient graph kernels by randomization
 Flach, P.A.; Bie, T.; Cristianini, N.: Machine learning and knowledge discovery in databases. European conference, ECML PKDD 2012. Pt.1 : Bristol, UK, September 2428, 2012; proceedings Berlin: Springer, 2012 (Lecture Notes in Computer Science 7523) ISBN: 9783642334597 ISBN: 3642334598 ISBN: 9783642334603 S.378393 
 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD) <2012, Bristol> 

 Englisch 
 Konferenzbeitrag 
 Fraunhofer IAIS () 
Abstract
Learning from complex data is becoming increasingly important, and graph kernels have recently evolved into a rapidly developing branch of learning on structured data. However, previously proposed kernels rely on having discrete node label information. In this paper, we explore the power of continuous nodelevel features for propagationbased graph kernels. Specifically, propagation kernels exploit node label distributions from propagation schemes like label propagation, which naturally enables the construction of graph kernels for partially labeled graphs. In order to efficiently extract graph features from continuous node label distributions, and in general from continuous vectorvalued node attributes, we utilize randomized techniques, which easily allow for deriving similarity measures based on propagated information. We show that propagation kernels utilizing localitysensitive hashing reduce the runtime of existing graph kernels by several orders of magnitude. We evaluate the performance of various propagation kernels on realworld bioinformatics and image benchmark datasets.