Information Science and Engineering Lab

We perform teaching and research in machine learning strategies for the pattern analysis of various kinds of data. This comprises statistical models for clustering, graphical models for network inference and algorithmic methods to efficiently find these structures in the data.

Contact Info
CAB F 61.1
Universitaetstrasse 6,
8092 Zurich
Schweiz

+41 44 632 64 96

Learn More

Follow Us

Information Science and Engineering Lab

We perform teaching and research in machine learning strategies for the pattern analysis of various kinds of data. This comprises statistical models for clustering, graphical models for network inference and algorithmic methods to efficiently find these structures in the data.

Contact Info
CAB F 61.1
Universitaetstrasse 6,
8092 Zurich
Schweiz

+41 44 632 64 96

Learn More

Follow Us

Publications - page 21

2007 Other Journal Item

Fully automatic registration of electron microscopy images with high and low resolution

Verena Kaynig, Bernd Fischer, Roger Wepf, Joachim M. Buhmann,

Microscopy and Microanalysis, 13

Research Collection

2007 Conference Paper

Compositional object recognition, segmentation, and tracking in video

Björn Ommer, Joachim M. Buhmann,

6th International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition, 4679

Research Collection

2007 Master Thesis

Cluster analysis of heterogeneous rank data

Ludwig M. Busse, Peter Orbanz, Joachim M. Buhmann,

International Machine Learning Workshop,

DOI: 10.3929/ethz-a-005541820      Research Collection

Clusteranalyse + Klassifikation (mathematische Statistik) Rangstatistik + Selektion (mathematische Statistik) Unvollständige Daten Und Fehlende Daten (mathematische Statistik) Cluster Analysis + Classification (mathematical Statistics) Ranking + Selection (mathematical Statistics) Incomplete Data And Missing Data (mathematical Statistics)
2007 Conference Paper

Cluster analysis of heterogeneous rank data-2007

Ludwig M. Busse, Peter Orbanz, Joachim M. Buhmann,

24th International Conference on Machine Learning (ICML 2007), 227

Research Collection

2007 Conference Paper

Bayesian order-adaptive clustering for video segmentation

Peter Orbanz, Samuel Braendle, Joachim M. Buhmann,

6th International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR 2007), 4679

Research Collection

2007 Journal Article

A workflow to increase the detection rate of proteins from unsequenced organisms in high-throughput proteomics experiments

Jonas Grossmann, Bernd Fischer, Katja Baerenfaller, Judith Owiti, Joachim M. Buhmann, Wilhelm Gruissem, Sacha Baginsky,

Proteomics, 7

Research Collection

De Novo Peptide Sequencing Error Tolerant Blast Searches Plant Proteomics Spectrum Quality Scoring
2006 Conference Paper

Smooth Image Segmentation by Nonparametric Bayesian Inference

Peter Orbanz, Joachim M. Buhmann,

9th European Conference on Computer Vision 2006 (ECCV 2006), 3951

Research Collection

2006 Conference Paper

Semi-supervised LC/MS alignment for differential proteomics

Bernd Fischer, Jonas Grossmann, Volker Roth, Wilhelm Gruissem, Sacha Baginsky, Joachim M. Buhmann,

14th Conference on Intelligent Systems for Molecular Biology 2006 (ISMB 2006), 22

DOI: 10.3929/ethz-b-000022631      Research Collection

2006 Conference Paper

Probabilistic De Novo Peptide Sequencing with Doubly Charged Ion

Hansruedi Peter, Bernd Fischer, Joachim M. Buhmann,

28th DAGM Symposium 2006, 4174

Research Collection

2006 Journal Article

On the information and representation of non-Euclidean pairwise data

Julian Laub, Volker Roth, Joachim M. Buhmann, Klaus-Robert Müller,

Pattern Recognition, 39

Research Collection

Non Euclidean Pairwise Data Embedding Visualization Multidimensional Scaling