BibTeX Export for Dagstuhl Seminar Proceedings, Volume 7131

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@InProceedings{biehl_et_al:DagSemProc.07131.1,
  author =	{Biehl, Michael and Hammer, Barbara and Verleysen, Michel and Villmann, Thomas},
  title =	{{07131 Abstracts Collection – Similarity-based Clustering and its Application to Medicine and Biology}},
  booktitle =	{Similarity-based Clustering and its Application to Medicine and Biology},
  pages =	{1--12},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2007},
  volume =	{7131},
  editor =	{Michael Biehl and Barbara Hammer and Michel Verleysen and Thomas Villmann},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/DagSemProc.07131.1},
  URN =		{urn:nbn:de:0030-drops-11190},
  doi =		{10.4230/DagSemProc.07131.1},
  annote =	{Keywords: Similarity-based clustering and classification, prototype-based classifiers, self-organisation, SOM, learning vector quantization, medical diagnosis, bioinformatics}
}
@InProceedings{biehl_et_al:DagSemProc.07131.2,
  author =	{Biehl, Michael and Hammer, Barbara and Verleysen, Michel and Villmann, Thomas},
  title =	{{07131 Summary – Similarity-based Clustering and its Application to Medicine and Biology}},
  booktitle =	{Similarity-based Clustering and its Application to Medicine and Biology},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2007},
  volume =	{7131},
  editor =	{Michael Biehl and Barbara Hammer and Michel Verleysen and Thomas Villmann},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/DagSemProc.07131.2},
  URN =		{urn:nbn:de:0030-drops-11177},
  doi =		{10.4230/DagSemProc.07131.2},
  annote =	{Keywords: Clustering, bioinformatics, medicine}
}
@InProceedings{schleif:DagSemProc.07131.3,
  author =	{Schleif, Frank Michael},
  title =	{{Advances in pre-processing and model generation for mass spectrometric data analysis}},
  booktitle =	{Similarity-based Clustering and its Application to Medicine and Biology},
  pages =	{1--24},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2007},
  volume =	{7131},
  editor =	{Michael Biehl and Barbara Hammer and Michel Verleysen and Thomas Villmann},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/DagSemProc.07131.3},
  URN =		{urn:nbn:de:0030-drops-11329},
  doi =		{10.4230/DagSemProc.07131.3},
  annote =	{Keywords: Similarity measures, functional data, proteomics, mass spectrometry, pre-processing,wavelet analysis, generalized peak list}
}
@InProceedings{strickert_et_al:DagSemProc.07131.4,
  author =	{Strickert, Marc and Seiffert, Udo},
  title =	{{Correlation-based Data Representation}},
  booktitle =	{Similarity-based Clustering and its Application to Medicine and Biology},
  pages =	{1--16},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2007},
  volume =	{7131},
  editor =	{Michael Biehl and Barbara Hammer and Michel Verleysen and Thomas Villmann},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/DagSemProc.07131.4},
  URN =		{urn:nbn:de:0030-drops-11347},
  doi =		{10.4230/DagSemProc.07131.4},
  annote =	{Keywords: Correlation, data representation, gradient-based optimization, clustering, neural gas}
}
@InProceedings{witoelar_et_al:DagSemProc.07131.5,
  author =	{Witoelar, Aree and Biehl, Michael and Hammer, Barbara},
  title =	{{Learning Vector Quantization: generalization ability and dynamics of competing prototypes}},
  booktitle =	{Similarity-based Clustering and its Application to Medicine and Biology},
  pages =	{1--11},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2007},
  volume =	{7131},
  editor =	{Michael Biehl and Barbara Hammer and Michel Verleysen and Thomas Villmann},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/DagSemProc.07131.5},
  URN =		{urn:nbn:de:0030-drops-11311},
  doi =		{10.4230/DagSemProc.07131.5},
  annote =	{Keywords: Online learning, learning vector quantization}
}
@InProceedings{hammer_et_al:DagSemProc.07131.6,
  author =	{Hammer, Barbara and Hasenfuss, Alexander},
  title =	{{Relational Clustering}},
  booktitle =	{Similarity-based Clustering and its Application to Medicine and Biology},
  pages =	{1--15},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2007},
  volume =	{7131},
  editor =	{Michael Biehl and Barbara Hammer and Michel Verleysen and Thomas Villmann},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/DagSemProc.07131.6},
  URN =		{urn:nbn:de:0030-drops-11182},
  doi =		{10.4230/DagSemProc.07131.6},
  annote =	{Keywords: Neural gas, dissimilarity data}
}
@InProceedings{schneider:DagSemProc.07131.7,
  author =	{Schneider, Petra},
  title =	{{Relevance Matrices in LVQ}},
  booktitle =	{Similarity-based Clustering and its Application to Medicine and Biology},
  pages =	{1--6},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2007},
  volume =	{7131},
  editor =	{Michael Biehl and Barbara Hammer and Michel Verleysen and Thomas Villmann},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/DagSemProc.07131.7},
  URN =		{urn:nbn:de:0030-drops-11332},
  doi =		{10.4230/DagSemProc.07131.7},
  annote =	{Keywords: Learning Vector Quantization, Relevance Learning, adaptive distance measure}
}

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