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1998
1999
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Regression
Models for Ordinal Data: A Machine Learning Approach, Research Student
Conference, Bristol, 1999.
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Classification
on Proximity Data with LP- and QP-Machines, Royal Holloway College,
Egham, 1999.
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Size Does
Matter - Learning with Margins, Technical University of Berlin, 1999.
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Bayes
Point Machines: Estimating the Bayes Point in Kernel Space, Microsoft
Research Cambridge, 1999.
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Support
Vector Learning for Ordinal Regression, ICANN, 1999.
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A
PAC-Bayesian Bound for Linear SVMs, ICANN, Workshop on Kernel Methods,
GPs and SVMs, 1999.
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A
PAC-Bayesian Bound for SVMs, NIPS*99, Workshop on SVMs, 1999.
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Robust Bayes Point Machines, NIPS*99, Workshop on SVMs, 1999.
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VC and
PAC-Bayesian Transduction, Lecture Slides, Technical University
Berlin, 1999.
2000
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Statistical
Learning Theory - Future Perspectives, Lecture Slides, Technical
University Berlin, 2000.
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Generalisation Error Bounds for Sparse Linear Classifiers, COLT 2000,
mixture of two talks, 2000.
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Learning Theory and Kernel Practice, NIPS*2000, Workshop of New
Perspectives in Kernel Methods, 2000.
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The Kernel Gibbs Sampler, NIPS*2000 Poster, 2000.
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Large Scale Bayes Point Machines, NIPS*2000 Poster, 2000.
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A PAC-Bayesian Margin Bound for Linear Classifiers: Why SVMs work,
NIPS*2000 Poster, 2000.
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From Margin To Sparsity, NIPS*2000 Poster, 2000.
2001
2002
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Perceptron Meets Reuters, Natural Computing Application Forum, 2002.
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Learning Kernel Classifiers: Algorithms, Tutorial at the Gatsby
Computational Neuroscience Unit, 2002.
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Learning Kernel Classifiers: Theory (part
I, part II), Tutorial at the Gatsby Computational Neuroscience Unit,
2002.
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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.
2003
2006
This page has not been regularly updated since 2003. If you are interested in
accompanying material to any of my work (see Research),
please contact me at
rherb@microsoft.com.
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