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Empirical Inference Talk 2008

Logistic Regression for Graph Classification

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Empirical Inference
Nino Shervashidze
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Empirical Inference
Koji Tsuda

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
Bibtex Type: Talk (talk)
Digital: 0
Electronic Archiving: grant_archive
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:

BibTex 

@talk{5685,
  title = {Logistic Regression for Graph Classification},
  abstract = {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.},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  month = dec,
  year = {2008},
  slug = {5685},
  author = {Shervashidze, N. and Tsuda, K.},
  month_numeric = {12}
}