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Logistic Regression for Graph Classification




In this paper we deal with graph classification. We propose a new algorithm for performing sparse logistic regression for graphs, which is comparable in accuracy with other methods of graph classification and produces probabilistic output in addition. Sparsity is required for the reason of interpretability, which is often necessary in domains such as bioinformatics or chemoinformatics.

Author(s): Shervashidze, N. and Tsuda, K.
Year: 2008
Month: December
Day: 0

Department(s): Empirical Inference
Bibtex Type: Talk (talk)

Digital: 0
Event Name: NIPS 2008 Workshop on "Structured Input - Structured Output" (NIPS SISO 2008)
Event Place: Whistler, BC, Canada
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: Web


  title = {Logistic Regression for Graph Classification},
  author = {Shervashidze, N. and Tsuda, K.},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  month = dec,
  year = {2008},
  month_numeric = {12}