Articles

  • Ralf Herbrich, Thore Graepel, "A PAC-Bayesian Margin Bound for Linear Classifiers" A PAC-Bayesian Margin Bound for Linear Classifiershttp://www.research.microsoft.com/~rherb/papers/hergrae02.ps.gz
  • Ralf Herbrich, Robert C. Williamson, "Algorithmic Luckiness" Algorithmic Luckinesshttp://www.research.microsoft.com/~rherb/papers/herwill02b.ps.gz
  • Ralf Herbrich, Thore Graepel, Colin Campbell, "Bayes Point Machines" Bayes Point Machineshttp://www.research.microsoft.com/~rherb/papers/hergraecamp01.ps.gz
  • Jason Weston, Ralf Herbrich, "Adaptive Margin Support Vector Machines" Adaptive Margin Support Vector Machineshttp://www.research.microsoft.com/~rherb/papers/wesher99.ps.gz
  • Ralf Herbrich, Thore Graepel, Klaus Obermayer, "Large Margin Rank Boundaries for Ordinal Regression" Large Margin Rank Boundaries for Ordinal Regressionhttp://www.research.microsoft.com/~rherb/papers/herobergrae99.ps.gz
  • Ralf Herbrich, Max Keilbach, Thore Graepel, Peter Bollmann--Sdorra, Klaus Obermayer, "Neural Networks in Economics: Background, Applications, and New Developments" Neural Networks in Economics: Background, Applications, and New Developmentshttp://www.research.microsoft.com/~rherb/papers/herkeilgraebollober99.ps.gz
  • Tobias Scheffer, Ralf Herbrich, Fritz Wysotzki, "Efficient theta-subsumption based on graph algorithms" Efficient theta-subsumption based on graph algorithms

In Proceedings

  • Neil Lawrence, Matthias Seeger, Ralf Herbrich, "Fast Sparse Gaussian Process Methods: The Informative Vector Machine" Fast Sparse Gaussian Process Methods: The Informative Vector Machinehttp://www.research.microsoft.com/~rherb/papers/lawseeher.ps.gz
  • Ralf Herbrich, Robert C. Williamson, "Algorithmic Luckiness" Algorithmic Luckinesshttp://www.research.microsoft.com/~rherb/papers/herwill01.ps.gz
  • Simon Hill, Hugo Zaragoza, Ralf Herbrich, Peter J. Rayner, "Average Precision and the Problem of Generalisation" Average Precision and the Problem of Generalisationhttp://www.research.microsoft.com/~rherb/papers/HilZarHerRay02.ps.gz
  • Yaoyong Li, Hugo Zaragoza, Ralf Herbrich, John Shawe-Taylor, Jaz Kandola, "The Perceptron Algorithm with Uneven Margins" The Perceptron Algorithm with Uneven Marginshttp://www.research.microsoft.com/~rherb/papers/LiZarHerShaKan02.ps.gz
  • Bernhard Sch�lkopf, Ralf Herbrich, Alexander J. Smola, "A Generalized Representer Theorem" A Generalized Representer Theoremhttp://www.research.microsoft.com/~rherb/papers/schoehersmo01.ps.gz
  • Bernhard Sch�lkopf, Ralf Herbrich, Alexander J. Smola, "A Generalized Representer Theorem" A Generalized Representer Theoremhttp://www.research.microsoft.com/~rherb/papers/schoehersmo01.ps.gz
  • Ralf Herbrich, Thore Graepel, "A PAC-Bayesian Margin Bound for Linear Classifiers: Why SVMs work" A PAC-Bayesian Margin Bound for Linear Classifiers: Why SVMs workhttp://www.research.microsoft.com/~rherb/papers/hergrae00b.ps.gz
  • Thore Graepel, Ralf Herbrich, Robert C. Williamson, "From Margin to Sparsity" From Margin to Sparsityhttp://www.research.microsoft.com/~rherb/papers/graeherwill00.ps.gz
  • Ralf Herbrich, Thore Graepel, "Large Scale Bayes Point Machines" Large Scale Bayes Point Machineshttp://www.research.microsoft.com/~rherb/papers/hergrae00c.ps.gz
  • Thore Graepel, Mike Goutrie, Marco Kr�ger, Ralf Herbrich, "Learning on Graphs in the Game of Go" Learning on Graphs in the Game of Gohttp://www.research.microsoft.com/~rherb/papers/graegoukrueher01.ps.gz
  • Arthur Gretton, Arnaud Doucet, Ralf Herbrich, Peter J. W. Rayner, Bernhard Sch�lkopf, "Support Vector Regression for Black-Box System Identification" Support Vector Regression for Black-Box System Identificationhttp://www.research.microsoft.com/~rherb/papers/gredouherrayschoe01.ps.gz
  • Thore Graepel, Ralf Herbrich, "The Kernel Gibbs Sampler" The Kernel Gibbs Samplerhttp://www.research.microsoft.com/~rherb/papers/graeher00b.ps.gz
  • Thore Graepel, Ralf Herbrich, Klaus Obermayer, "Bayesian Transduction" Bayesian Transductionhttp://www.research.microsoft.com/~rherb/papers/graeherober99b.ps.gz
  • Thore Graepel, Ralf Herbrich, John Shawe-Taylor, "Generalisation Error Bounds for Sparse Linear Classifiers" Generalisation Error Bounds for Sparse Linear Classifiershttp://www.research.microsoft.com/~rherb/papers/graehertay00.ps.gz
  • Ralf Herbrich, Thore Graepel, Colin Campbell, "Robust Bayes Point Machines" Robust Bayes Point Machineshttp://www.research.microsoft.com/~rherb/papers/hergraecamp00.ps.gz
  • Ralf Herbrich, Thore Graepel, John Shawe-Taylor, "Sparsity vs. Large Margins for Linear Classifiers" Sparsity vs. Large Margins for Linear Classifiershttp://www.research.microsoft.com/~rherb/papers/hergraetay00.ps.gz
  • Ralf Herbrich, Jason Weston, "Adaptive Margin Support Vector Machines for Classification Learning" Adaptive Margin Support Vector Machines for Classification Learninghttp://www.research.microsoft.com/~rherb/papers/herwes99.ps.gz
  • Ralf Herbrich, Thore Graepel, Colin Campbell, "Bayes Point Machines: Estimating the Bayes Point in Kernel Space" Bayes Point Machines: Estimating the Bayes Point in Kernel Spacehttp://www.research.microsoft.com/~rherb/papers/hergraecamp99.ps.gz
  • Thore Graepel, Ralf Herbrich, Klaus Obermayer, "Bayesian transductive classification by maximizing volume in version space" Bayesian transductive classification by maximizing volume in version space
  • Thore Graepel, Ralf Herbrich, Peter Bollmann--Sdorra, Klaus Obermayer, "Classification on Pairwise Proximity Data" Classification on Pairwise Proximity Datahttp://www.research.microsoft.com/~rherb/papers/graeherbollober99.ps.gz
  • Thore Graepel, Ralf Herbrich, Bernhard Sch�lkopf, Alex Smola, Peter Bartlett, Klaus Robert--M�ller, Klaus Obermayer, Robert Williamson, "Classification on Proximity Data with LP--Machines" Classification on Proximity Data with LP--Machineshttp://www.research.microsoft.com/~rherb/papers/graeherschoesmobartmuelober99.ps.gz
  • Ralf Herbrich, Thore Graepel, Klaus Obermayer, "Support Vector Learning for Ordinal Regression" Support Vector Learning for Ordinal Regressionhttp://www.research.microsoft.com/~rherb/papers/hergraeober99b.ps.gz
  • Ralf Herbrich, Thore Graepel, Peter Bollmann--Sdorra, Klaus Obermayer, "Learning a Preference Relation in IR" Learning a Preference Relation in IRhttp://www.research.microsoft.com/~rherb/papers/hergraebollober98.ps.gz
  • Ralf Herbrich, Thore Graepel, Peter Bollmann--Sdorra, Klaus Obermayer, "Supervised Learning of Preference Relations" Supervised Learning of Preference Relationshttp://www.research.microsoft.com/~rherb/papers/hergraebollober98b.ps.gz
  • Ralf Herbrich, Tobias Scheffer, "Generation of task specific segmentation procedures as a model selection task" Generation of task specific segmentation procedures as a model selection taskhttp://stat.cs.tu-berlin.de/publications/papers/hersche97.ps.gz
  • Tobias Scheffer, Ralf Herbrich, "Unbiased Assessment of Learning Algorithms" Unbiased Assessment of Learning Algorithmshttp://ki.cs.tu-berlin.de/~scheffer/artikel/ijcai97.ps
  • Tobias Scheffer, Ralf Herbrich, Fritz Wysotzki, "Efficient theta-subsumption based on graph algorithms" Efficient theta-subsumption based on graph algorithmshttp://ki.cs.tu-berlin.de/~scheffer/artikel/ilp96.ps
  • Tobias Scheffer, Ralf Herbrich, Fritz Wysotzki, "Graph based subsumption algorithms for machine learning" Graph based subsumption algorithms for machine learning

Technical Reports

  • Bernhard Sch�lkopf, Ralf Herbrich, Alexander J. Smola, Robert C. Williamson, "A Generalized Representer Theorem" A Generalized Representer Theorem [.gz]
  • Ralf Herbrich, Thore Graepel, Colin Campbell, "Bayesian Learning in Reproducing Kernel Hilbert Spaces" Bayesian Learning in Reproducing Kernel Hilbert Spaces [.gz]
  • Ralf Herbrich, Thore Graepel, Klaus Obermayer, "Regression Models for Ordinal Data: A Machine Learning Approach" Regression Models for Ordinal Data: A Machine Learning Approach [.gz]
  • Ralf Herbrich, Eric Heymann, "Animation von flexiblen Objekte" Animation von flexiblen Objekte [.gz]