Ralf Herbrich: Slides
PRINCIPAL DEVELOPMENT MANAGER
.
Here you can find a downloadable list of my slides from talks I have been giving. You are free to use any of this material in courses.
This page has not been updated since 2003. If you are interested in accompanying material to any of my work (see Research), please contact me at rherb@microsoft.com.
Slides from 1998
Slides from 1999
- Regression Models for Ordinal Data: A Machine Learning Approach, Research Student Conference, Bristol, 1999.
- Classification on Proximity Data with LP- and QP-Machines, Royal Holloway College, Egham, 1999.
- Size Does Matter - Learning with Margins, Technical University of Berlin, 1999.
- Bayes Point Machines: Estimating the Bayes Point in Kernel Space, Microsoft Research Cambridge, 1999.
- Support Vector Learning for Ordinal Regression, ICANN, 1999.
- A PAC-Bayesian Bound for Linear SVMs, ICANN, Workshop on Kernel Methods, GPs and SVMs, 1999.
- A PAC-Bayesian Bound for SVMs, NIPS*99, Workshop on SVMs, 1999.
- Robust Bayes Point Machines, NIPS*99, Workshop on SVMs, 1999.
- VC and PAC-Bayesian Transduction, Lecture Slides, Technical University Berlin, 1999.
Slides from 2000
- Statistical Learning Theory - Future Perspectives, Lecture Slides, Technical University Berlin, 2000.
- Generalisation Error Bounds for Sparse Linear Classifiers, COLT 2000, mixture of two talks, 2000.
- Learning Theory and Kernel Practice, NIPS*2000, Workshop of New Perspectives in Kernel Methods, 2000.
- The Kernel Gibbs Sampler, NIPS*2000 Poster, 2000.
- Large Scale Bayes Point Machines, NIPS*2000 Poster, 2000.
- A PAC-Bayesian Margin Bound for Linear Classifiers: Why SVMs work, NIPS*2000 Poster, 2000.
- From Margin To Sparsity, NIPS*2000 Poster, 2000.
Slides from 2001
- Statistical Learning Theory and Its Application, German Statistics Society Meeting, 2001.
- A Generalised Representer Theorem, Conference on Computational Learning Theory, 2001.
- Algorithmic Luckiness, Dagstuhl Seminar on Model Selection and Inference Principles, 2001.
Slides from 2002
- Perceptron Meets Reuters, Natural Computing Application Forum, 2002.
- Learning Kernel Classifiers: Algorithms, Tutorial at the Gatsby Computational Neuroscience Unit, 2002.
- Learning Kernel Classifiers: Theory (part I, part II), Tutorial at the Gatsby Computational Neuroscience Unit, 2002.
- A Statistical Analysis of the Precision-Recall Graph (poster), NIPS*2002 Workshop "Beyond Classification and Regression: Learning Rankings, Preferences, Equality Predicates, and Other Structures", 2002.
Slides from 2003
- Semidefinite Programming Machines, NIPS*2003 talk slides and poster slides, 2003.
- Semidefinite Programming by Perceptron Learning, NIPS*2003 Poster, 2003.
