15 Search Results for "Biehl, Michael"


Document
Integration of Expert Knowledge for Interpretable Models in Biomedical Data Analysis (Dagstuhl Seminar 16261)

Authors: Gyan Bhanot, Michael Biehl, Thomas Villmann, and Dietlind Zühlke

Published in: Dagstuhl Reports, Volume 6, Issue 6 (2016)


Abstract
This report documents the talks, discussions and outcome of the Dagstuhl seminar 16261 “Integration of Expert Knowledge for Interpretable Models in Biomedical Data Analysis”. The seminar brought together 37 participants from three diverse disciplines, who would normally not have opportunities to meet in such a forum, let alone discuss common interests and plan joint projects.

Cite as

Gyan Bhanot, Michael Biehl, Thomas Villmann, and Dietlind Zühlke. Integration of Expert Knowledge for Interpretable Models in Biomedical Data Analysis (Dagstuhl Seminar 16261). In Dagstuhl Reports, Volume 6, Issue 6, pp. 88-110, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2016)


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@Article{bhanot_et_al:DagRep.6.6.88,
  author =	{Bhanot, Gyan and Biehl, Michael and Villmann, Thomas and Z\"{u}hlke, Dietlind},
  title =	{{Integration of Expert Knowledge for Interpretable Models in Biomedical Data Analysis (Dagstuhl Seminar 16261)}},
  pages =	{88--110},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2016},
  volume =	{6},
  number =	{6},
  editor =	{Bhanot, Gyan and Biehl, Michael and Villmann, Thomas and Z\"{u}hlke, Dietlind},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/DagRep.6.6.88},
  URN =		{urn:nbn:de:0030-drops-67565},
  doi =		{10.4230/DagRep.6.6.88},
  annote =	{Keywords: Biomedical data analysis, Data visualization, Expert interactions, Feature selection and dimensionality reduction, Knowledge integration, Modeling}
}
Document
Learning in the context of very high dimensional data (Dagstuhl Seminar 11341)

Authors: Michael Biehl, Barbara Hammer, Erzsébet Merényi, Alessandro Sperduti, and Thomas Villman

Published in: Dagstuhl Reports, Volume 1, Issue 8 (2011)


Abstract
This report documents the program and the outcomes of Dagstuhl Seminar 11341 "Learning in the context of very high dimensional data". The aim of the seminar was to bring together researchers who develop, investigate, or apply machine learning methods for very high dimensional data to advance this important field of research. The focus was be on broadly applicable methods and processing pipelines, which offer efficient solutions for high-dimensional data analysis appropriate for a wide range of application scenarios.

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Michael Biehl, Barbara Hammer, Erzsébet Merényi, Alessandro Sperduti, and Thomas Villman. Learning in the context of very high dimensional data (Dagstuhl Seminar 11341). In Dagstuhl Reports, Volume 1, Issue 8, pp. 67-95, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2011)


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@Article{biehl_et_al:DagRep.1.8.67,
  author =	{Biehl, Michael and Hammer, Barbara and Mer\'{e}nyi, Erzs\'{e}bet and Sperduti, Alessandro and Villman, Thomas},
  title =	{{Learning in the context of very high dimensional data (Dagstuhl Seminar 11341)}},
  pages =	{67--95},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2011},
  volume =	{1},
  number =	{8},
  editor =	{Biehl, Michael and Hammer, Barbara and Mer\'{e}nyi, Erzs\'{e}bet and Sperduti, Alessandro and Villman, Thomas},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/DagRep.1.8.67},
  URN =		{urn:nbn:de:0030-drops-33125},
  doi =		{10.4230/DagRep.1.8.67},
  annote =	{Keywords: Curse of dimensionality, Dimensionality reduction, Regularization Deep learning, Visualization}
}
Document
Some steps towards a general principle for dimensionality reduction mappings

Authors: Barbara Hammer, Kerstin Bunte, and Michael Biehl

Published in: Dagstuhl Seminar Proceedings, Volume 10302, Learning paradigms in dynamic environments (2010)


Abstract
In the past years, many dimensionality reduction methods have been established which allow to visualize high dimensional data sets. Recently, also formal evaluation schemes have been proposed for data visualization, which allow a quantitative evaluation along general principles. Most techniques provide a mapping of a priorly given finite set of points only, requiring additional steps for out-of-sample extensions. We propose a general view on dimensionality reduction based on the concept of cost functions, and, based on this general principle, extend dimensionality reduction to explicit mappings of the data manifold. This offers the possibility of simple out-of-sample extensions. Further, it opens a way towards a theory of data visualization taking the perspective of its generalization ability to new data points. We demonstrate the approach based in a simple example.

Cite as

Barbara Hammer, Kerstin Bunte, and Michael Biehl. Some steps towards a general principle for dimensionality reduction mappings. In Learning paradigms in dynamic environments. Dagstuhl Seminar Proceedings, Volume 10302, pp. 1-15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2010)


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@InProceedings{hammer_et_al:DagSemProc.10302.5,
  author =	{Hammer, Barbara and Bunte, Kerstin and Biehl, Michael},
  title =	{{Some steps towards a general principle for dimensionality reduction mappings}},
  booktitle =	{Learning paradigms in dynamic environments},
  pages =	{1--15},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2010},
  volume =	{10302},
  editor =	{Barbara Hammer and Pascal Hitzler and Wolfgang Maass and Marc Toussaint},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/DagSemProc.10302.5},
  URN =		{urn:nbn:de:0030-drops-28034},
  doi =		{10.4230/DagSemProc.10302.5},
  annote =	{Keywords: Visualization, dimensionality reduction}
}
Document
09081 Abstracts Collection – Similarity-based learning on structures

Authors: Michael Biehl, Barbara Hammer, Sepp Hochreiter, Stefan C. Kremer, and Thomas Villmann

Published in: Dagstuhl Seminar Proceedings, Volume 9081, Similarity-based learning on structures (2009)


Abstract
From 15.02. to 20.02.2009, the Dagstuhl Seminar 09081 ``Similarity-based learning on structures '' was held in Schloss Dagstuhl~--~Leibniz Center for Informatics. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available.

Cite as

Michael Biehl, Barbara Hammer, Sepp Hochreiter, Stefan C. Kremer, and Thomas Villmann. 09081 Abstracts Collection – Similarity-based learning on structures. In Similarity-based learning on structures. Dagstuhl Seminar Proceedings, Volume 9081, pp. 1-15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2009)


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@InProceedings{biehl_et_al:DagSemProc.09081.1,
  author =	{Biehl, Michael and Hammer, Barbara and Hochreiter, Sepp and Kremer, Stefan C. and Villmann, Thomas},
  title =	{{09081 Abstracts Collection – Similarity-based learning on structures}},
  booktitle =	{Similarity-based learning on structures},
  pages =	{1--15},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2009},
  volume =	{9081},
  editor =	{Michael Biehl and Barbara Hammer and Sepp Hochreiter and Stefan C. Kremer 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.09081.1},
  URN =		{urn:nbn:de:0030-drops-20395},
  doi =		{10.4230/DagSemProc.09081.1},
  annote =	{Keywords: Similarity-based clustering and classification, metric adaptation and kernel design, learning on graphs, spatiotemporal data}
}
Document
09081 Summary – Similarity-based learning on structures

Authors: Michael Biehl, Barbara Hammer, Sepp Hochreiter, Stefan C. Kremer, and Thomas Villmann

Published in: Dagstuhl Seminar Proceedings, Volume 9081, Similarity-based learning on structures (2009)


Abstract
The seminar centered around different aspects of similarity-based clustering with the special focus on structures. This included theoretical foundations, new algorithms, innovative applications, and future challenges for the field.

Cite as

Michael Biehl, Barbara Hammer, Sepp Hochreiter, Stefan C. Kremer, and Thomas Villmann. 09081 Summary – Similarity-based learning on structures. In Similarity-based learning on structures. Dagstuhl Seminar Proceedings, Volume 9081, pp. 1-4, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2009)


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@InProceedings{biehl_et_al:DagSemProc.09081.2,
  author =	{Biehl, Michael and Hammer, Barbara and Hochreiter, Sepp and Kremer, Stefan C. and Villmann, Thomas},
  title =	{{09081 Summary – Similarity-based learning on structures}},
  booktitle =	{Similarity-based learning on structures},
  pages =	{1--4},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2009},
  volume =	{9081},
  editor =	{Michael Biehl and Barbara Hammer and Sepp Hochreiter and Stefan C. Kremer 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.09081.2},
  URN =		{urn:nbn:de:0030-drops-20382},
  doi =		{10.4230/DagSemProc.09081.2},
  annote =	{Keywords: Similarity-based clustering and classification, metric adaptation and kernel design, learning on graphs, spatiotemporal data}
}
Document
An adaptive model for learning molecular endpoints

Authors: Ian Walsh, Alessandro Vullo, and Gianluca Pollastri

Published in: Dagstuhl Seminar Proceedings, Volume 9081, Similarity-based learning on structures (2009)


Abstract
I will describe a recursive neural network that deals with undirected graphs, and its application to predicting property labels or activity values of small molecules. The model is entirely general, in that it can process any undirected graph with a finite number of nodes by factorising it into a number of directed graphs with the same skeleton. The model's only input in the applications I will present is the graph representing the chemical structure of the molecule. In spite of its simplicity, the model outperforms or matches the state of the art in three of the four tasks, and in the fourth is outperformed only by a method resorting to a very problem-specific feature.

Cite as

Ian Walsh, Alessandro Vullo, and Gianluca Pollastri. An adaptive model for learning molecular endpoints. In Similarity-based learning on structures. Dagstuhl Seminar Proceedings, Volume 9081, pp. 1-16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2009)


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@InProceedings{walsh_et_al:DagSemProc.09081.3,
  author =	{Walsh, Ian and Vullo, Alessandro and Pollastri, Gianluca},
  title =	{{An adaptive model for learning molecular endpoints}},
  booktitle =	{Similarity-based learning on structures},
  pages =	{1--16},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2009},
  volume =	{9081},
  editor =	{Michael Biehl and Barbara Hammer and Sepp Hochreiter and Stefan C. Kremer 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.09081.3},
  URN =		{urn:nbn:de:0030-drops-20367},
  doi =		{10.4230/DagSemProc.09081.3},
  annote =	{Keywords: Recursive neural networks, qsar, qspr, small molecules}
}
Document
Analysis of Robust Soft Learning Vector Quantization and an application to Facial Expression Recognition

Authors: Gert-Jan de Vries and Michael Biehl

Published in: Dagstuhl Seminar Proceedings, Volume 9081, Similarity-based learning on structures (2009)


Abstract
Learning Vector Quantization (LVQ) is a popular method for multiclass classification. Several variants of LVQ have been developed recently, of which Robust Soft Learning Vector Quantization (RSLVQ) is a promising one. Although LVQ methods have an intuitive design with clear updating rules, their dynamics are not yet well understood. In simulations within a controlled environment RSLVQ performed very close to optimal. This controlled environment enabled us to perform a mathematical analysis as a first step in obtaining a better theoretical understanding of the learning dynamics. In this talk I will discuss the theoretical analysis and its results. Moreover, I will focus on the practical application of RSLVQ to a real world dataset containing extracted features from facial expression data.

Cite as

Gert-Jan de Vries and Michael Biehl. Analysis of Robust Soft Learning Vector Quantization and an application to Facial Expression Recognition. In Similarity-based learning on structures. Dagstuhl Seminar Proceedings, Volume 9081, pp. 1-5, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2009)


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@InProceedings{devries_et_al:DagSemProc.09081.4,
  author =	{de Vries, Gert-Jan and Biehl, Michael},
  title =	{{Analysis of Robust Soft Learning Vector Quantization and an application to Facial Expression Recognition}},
  booktitle =	{Similarity-based learning on structures},
  pages =	{1--5},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2009},
  volume =	{9081},
  editor =	{Michael Biehl and Barbara Hammer and Sepp Hochreiter and Stefan C. Kremer 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.09081.4},
  URN =		{urn:nbn:de:0030-drops-20356},
  doi =		{10.4230/DagSemProc.09081.4},
  annote =	{Keywords: Learning Vector Quantization, Analysis, Facial Expression Recognition}
}
Document
Estimating Time Delay in Gravitationally Lensed Fluxes

Authors: Peter Tino, Juan C. Cuevas-Tello, and Somak Raychaudhury

Published in: Dagstuhl Seminar Proceedings, Volume 9081, Similarity-based learning on structures (2009)


Abstract
We study the problem of estimating the time delay between two signals representing delayed, irregularly sampled and noisy versions of the same underlying pattern. We propose a kernel-based technique in the context of an astronomical problem, namely estimating the time delay between two gravitationally lensed signals from a distant quasar. We test the algorithm on several artificial data sets, and also on real astronomical observations. By carrying out a statistical analysis of the results we present a detailed comparison of our method with the most popular methods for time delay estimation in astrophysics. Our method yields more accurate and more stable time delay estimates. Our methodology can be readily applied to current state-of-the-art optical monitoring data in astronomy, but can also be applied in other disciplines involving similar time series data.

Cite as

Peter Tino, Juan C. Cuevas-Tello, and Somak Raychaudhury. Estimating Time Delay in Gravitationally Lensed Fluxes. In Similarity-based learning on structures. Dagstuhl Seminar Proceedings, Volume 9081, pp. 1-3, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2009)


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@InProceedings{tino_et_al:DagSemProc.09081.5,
  author =	{Tino, Peter and Cuevas-Tello, Juan C. and Raychaudhury, Somak},
  title =	{{Estimating Time Delay in Gravitationally Lensed Fluxes}},
  booktitle =	{Similarity-based learning on structures},
  pages =	{1--3},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2009},
  volume =	{9081},
  editor =	{Michael Biehl and Barbara Hammer and Sepp Hochreiter and Stefan C. Kremer 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.09081.5},
  URN =		{urn:nbn:de:0030-drops-20378},
  doi =		{10.4230/DagSemProc.09081.5},
  annote =	{Keywords: Time series, kernel regression, statistical analysis, evolutionary algorithms, mixed representation}
}
Document
Advances in pre-processing and model generation for mass spectrometric data analysis

Authors: Frank Michael Schleif

Published in: Dagstuhl Seminar Proceedings, Volume 7131, Similarity-based Clustering and its Application to Medicine and Biology (2007)


Abstract
The analysis of complex signals as obtained by mass spectrometric measurements is complicated and needs an appropriate representation of the data. Thereby the kind of preprocessing, feature extraction as well as the used similarity measure are of particular importance. Focusing on biomarker analysis and taking the functional nature of the data into account this task is even more complicated. A new mass spectrometry tailored data preprocessing is shown, discussed and analyzed in a clinical proteom study compared to a standard setting.

Cite as

Frank Michael Schleif. Advances in pre-processing and model generation for mass spectrometric data analysis. In Similarity-based Clustering and its Application to Medicine and Biology. Dagstuhl Seminar Proceedings, Volume 7131, pp. 1-24, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2007)


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@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}
}
Document
Correlation-based Data Representation

Authors: Marc Strickert and Udo Seiffert

Published in: Dagstuhl Seminar Proceedings, Volume 7131, Similarity-based Clustering and its Application to Medicine and Biology (2007)


Abstract
The Dagstuhl Seminar 'Similarity-based Clustering and its Application to Medicine and Biology' (07131) held in March 25--30, 2007, provided an excellent atmosphere for in-depth discussions about the research frontier of computational methods for relevant applications of biomedical clustering and beyond. We address some highlighted issues about correlation-based data analysis in this seminar postribution. First, some prominent correlation measures are briefly revisited. Then, a focus is put on Pearson correlation, because of its widespread use in biomedical sciences and because of its analytic accessibility. A connection to Euclidean distance of z-score transformed data outlined. Cost function optimization of correlation-based data representation is discussed for which, finally, applications to visualization and clustering of gene expression data are given.

Cite as

Marc Strickert and Udo Seiffert. Correlation-based Data Representation. In Similarity-based Clustering and its Application to Medicine and Biology. Dagstuhl Seminar Proceedings, Volume 7131, pp. 1-16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2007)


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@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}
}
Document
Learning Vector Quantization: generalization ability and dynamics of competing prototypes

Authors: Aree Witoelar, Michael Biehl, and Barbara Hammer

Published in: Dagstuhl Seminar Proceedings, Volume 7131, Similarity-based Clustering and its Application to Medicine and Biology (2007)


Abstract
Learning Vector Quantization (LVQ) are popular multi-class classification algorithms. Prototypes in an LVQ system represent the typical features of classes in the data. Frequently multiple prototypes are employed for a class to improve the representation of variations within the class and the generalization ability. In this paper, we investigate the dynamics of LVQ in an exact mathematical way, aiming at understanding the influence of the number of prototypes and their assignment to classes. The theory of on-line learning allows a mathematical description of the learning dynamics in model situations. We demonstrate using a system of three prototypes the different behaviors of LVQ systems of multiple prototype and single prototype class representation.

Cite as

Aree Witoelar, Michael Biehl, and Barbara Hammer. Learning Vector Quantization: generalization ability and dynamics of competing prototypes. In Similarity-based Clustering and its Application to Medicine and Biology. Dagstuhl Seminar Proceedings, Volume 7131, pp. 1-11, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2007)


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@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}
}
Document
Relevance Matrices in LVQ

Authors: Petra Schneider

Published in: Dagstuhl Seminar Proceedings, Volume 7131, Similarity-based Clustering and its Application to Medicine and Biology (2007)


Abstract
LVQ-networks belong to the class of distance-based classifiers. The underlying distance measure is of special importance for their performance, because it defines how the data items are compared and how they are grouped in clusters. Relevance Learning techniques try to adapt the distance measure to the specific data used for training. I will present a new adaptive distance measure in Learning Vector Quantization which is an extension of previously proposed Relevance Learning schemes. In comparison to the already existing techniques for Relevance Learning, this distance measure is more powerful to represent the internal structure of the data appropriately. Two applications will be used to demonstrate the behavior of the new algorithm (artificial and real life).

Cite as

Petra Schneider. Relevance Matrices in LVQ. In Similarity-based Clustering and its Application to Medicine and Biology. Dagstuhl Seminar Proceedings, Volume 7131, pp. 1-6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2007)


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@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}
}
Document
07131 Abstracts Collection – Similarity-based Clustering and its Application to Medicine and Biology

Authors: Michael Biehl, Barbara Hammer, Michel Verleysen, and Thomas Villmann

Published in: Dagstuhl Seminar Proceedings, Volume 7131, Similarity-based Clustering and its Application to Medicine and Biology (2007)


Abstract
From 25.03. to 30.03.2007, the Dagstuhl Seminar 07131 ``Similarity-based Clustering and its Application to Medicine and Biology'' was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available.

Cite as

Michael Biehl, Barbara Hammer, Michel Verleysen, and Thomas Villmann. 07131 Abstracts Collection – Similarity-based Clustering and its Application to Medicine and Biology. In Similarity-based Clustering and its Application to Medicine and Biology. Dagstuhl Seminar Proceedings, Volume 7131, pp. 1-12, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2007)


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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}
}
Document
07131 Summary – Similarity-based Clustering and its Application to Medicine and Biology

Authors: Michael Biehl, Barbara Hammer, Michel Verleysen, and Thomas Villmann

Published in: Dagstuhl Seminar Proceedings, Volume 7131, Similarity-based Clustering and its Application to Medicine and Biology (2007)


Abstract
This paper summarizes presentations, discussions, and results of the Dagstuhl seminar.

Cite as

Michael Biehl, Barbara Hammer, Michel Verleysen, and Thomas Villmann. 07131 Summary – Similarity-based Clustering and its Application to Medicine and Biology. In Similarity-based Clustering and its Application to Medicine and Biology. Dagstuhl Seminar Proceedings, Volume 7131, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2007)


Copy BibTex To Clipboard

@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}
}
Document
Relational Clustering

Authors: Barbara Hammer and Alexander Hasenfuss

Published in: Dagstuhl Seminar Proceedings, Volume 7131, Similarity-based Clustering and its Application to Medicine and Biology (2007)


Abstract
We introduce relational variants of neural gas, a very efficient and powerful neural clustering algorithm. It is assumed that a similarity or dissimilarity matrix is given which stems from Euclidean distance or dot product, respectively, however, the underlying embedding of points is unknown. In this case, one can equivalently formulate batch optimization in terms of the given similarities or dissimilarities, thus providing a way to transfer batch optimization to relational data. Interestingly, convergence is guaranteed even for general symmetric and nonsingular metrics.

Cite as

Barbara Hammer and Alexander Hasenfuss. Relational Clustering. In Similarity-based Clustering and its Application to Medicine and Biology. Dagstuhl Seminar Proceedings, Volume 7131, pp. 1-15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2007)


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@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}
}
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