Logistic Regression for Graph Classification

Shervashidze, N; Tsuda, K

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Logistic Regression for Graph Classification

Shervashidze, N., & Tsuda, K. (2008). Logistic Regression for Graph Classification. In NIPS 2008: Mini Symposia & Workshops (pp. 75).

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-C643-0 Version Permalink: https://hdl.handle.net/21.11116/0000-0003-A0D1-B

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http://media.nips.cc/Conferences/2008/NIPSWorkshopsProgram2008.pdf (Abstract) Open Access status unknown

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Creators:
Shervashidze, N^{1, 2}, Author
Tsuda, K^{1, 2}, Author

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1Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497794
2Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795

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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.

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Dates: Date issued: 2008-12

Publication Status: Issued

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Identifiers: BibTex Citekey: 5685

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Title: NIPS 2008 Workshop: Structured Input - Structured Output (NIPS SISO 2008)

Place of Event: Whistler, BC, Canada

Start-/End Date: 2008-12-12

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Title: NIPS 2008: Mini Symposia & Workshops

Source Genre: Proceedings

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Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 75 Identifier: -