Markus Breitenbach
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My research is focused on high-dimensional, non-parametric learning and the use of manifolds for machine learning. My current research project focuses on utilizing manifold structures in unsupervised and semi-supervised learning and automatically determining optimal parameters for the algorithms (model selection).

 

Publications

Avleen Birjral, Markus Breitenbach, Gregory Grudic, Mixture of Watson Distributions: Generative Model for Hyperspherical Embeddings, accepted to AISTATS, 2007
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Avleen Bijral, Markus Breitenbach, Greg Grudic, Mixture of Watson Distributions: A Generative Model for Hyperspherical Embeddings, Accepted to AISTATS, 2007
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Markus Breitenbach, Tim Brennan, William Dieterich, Gregory Grudic, Clustering of Psychological Personality Tests of Criminal Offenders, ECML PKDD 2006 SAS Workshop on Practical Data Mining, 2006
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Tim Brennan, William Dieterich, Markus Breitenbach, Etiological patterns among female delinquents: Towards an explanatory taxonomy, accepted for presentation at the British Society of Criminology, Annual Conference, 2006
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Markus Breitenbach, Gregory Grudic, Clustering Through Ranking On Manifolds, accepted to ICML, 2005
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Markus Breitenbach, Greg Grudic, Clustering Through Ranking On Manifolds, ICML, 2005
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Sander Bohte, Markus Breitenbach, Gregory Grudic, Nonparametric Classification with Polynomial MPMC Cascades, accepted to ICML, 2004
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Sander Bohte, Markus Breitenbach, Gregory Grudic, Nonparametric Classification with Polynomial MPMC Cascades, available as Technical Report, 2004
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Markus Breitenbach, Rodney Nielsen, Gregory Grudic, Probabilistic Random Forests: Predicting Data Point Specific Misclassification Probabilities, Technical Report, 2004
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Markus Breitenbach, Greg Grudic, Clustering with Local and Global Consistency, Submitted, Also Available as Technical Report CU-CS-973-04, 2004
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Markus Breitenbach, Sander M. Bohte, Greg Grudic, Linear Time Nonparametric Classification and Feature Selection with Polynomial MPMC Cascades for large datasets, Submitted, Available as Technical Report CU-CS-977-04, 2004
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Sander M. Bohte, Markus Breitenbach, Greg Grudic, Nonparametric Classification with Polynomial MPMC Cascades, ICML, July 4-8 2004, Banff, Canada, 2004
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Markus Breitenbach, Rodney Nielsen, Greg Grudic, Probabilistic Random Forests: Predicting Data Point Specific Misclassification Probabilities, Technical Report CU-CS-954-03, 2003
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Markus Breitenbach, Gregory Grudic, Clustering with Local and Global Consistency, available as Technical Report, 2001
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Markus Breitenbach, Sander Bohte, Gregory Grudic, Linear Time Nonparametric Classification and Feature Selection with Polynomial MPMC Cascades for large datasets, available as Technical Report, 2001
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Frank Hutter, Markus Breitenbach, Harvesta - A new Approach in Information Management Suited for Corporate Intelligence, Presented at the 6th annual SCIP European Conference, 2001
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