Peter Sollich

Proceedings of the 2011 IEEE International Symposium on Information Theory Proceedings, 2011

2010

Subband acoustic waveform front-end for robust speech recognition using support vector machines.

[BibT_eX]

[DOI]

Proceedings of the 2010 IEEE Spoken Language Technology Workshop, 2010

Exact learning curves for Gaussian process regression on large random graphs.

[BibT_eX]

[DOI]

Matthew Urry

Proceedings of the Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010. Proceedings of a meeting held 6-9 December 2010, 2010

High-dimensional linear representations for robust speech recognition.

[BibT_eX]

[DOI]

Matthew Ager

Proceedings of the Information Theory and Applications Workshop, 2010

Towards robust phoneme classification with hybrid features.

[BibT_eX]

[DOI]

Proceedings of the IEEE International Symposium on Information Theory, 2010

2009

Kernels and learning curves for Gaussian process regression on random graphs.

[BibT_eX]

[DOI]

Matthew Urry

Camille Coti

Proceedings of the Advances in Neural Information Processing Systems 22: 23rd Annual Conference on Neural Information Processing Systems 2009. Proceedings of a meeting held 7-10 December 2009, 2009

Tuning support vector machines for robust phoneme classification with acoustic waveforms.

[BibT_eX]

[DOI]

Proceedings of the INTERSPEECH 2009, 2009

Custom-designed SVM kernels for improved robustness of phoneme classification.

[BibT_eX]

[DOI]

Proceedings of the 17th European Signal Processing Conference, 2009

Robust phoneme classification: Exploiting the adaptability of acoustic waveform models.

[BibT_eX]

[DOI]

Matthew Ager

Proceedings of the 17th European Signal Processing Conference, 2009

2008

Combined PLP - Acoustic waveform classification for robust phoneme recognition using support vector machines.

[BibT_eX]

[DOI]

Proceedings of the 2008 16th European Signal Processing Conference, 2008

Towards robust phoneme classification: Augmentation of PLP models with acoustic waveforms.

[BibT_eX]

[DOI]

Proceedings of the 2008 16th European Signal Processing Conference, 2008

2007

Phoneme Classification in Frequency Subbands using Ensemble Methods.

[BibT_eX]

[DOI]

Nicholas Betteridge

Proceedings of the 15th International Conference on Digital Signal Processing, 2007

Stable Belief Propagation in Gaussian Dags.

[BibT_eX]

[DOI]

Proceedings of the IEEE International Conference on Acoustics, 2007

2006

Robustness of phoneme classification in different representation spaces.

[BibT_eX]

[DOI]

Lena Khoo

Proceedings of the 14th European Signal Processing Conference, 2006

2005

Bayesian approach to feature selection and parameter tuning for support vector machine classifiers.

[BibT_eX]

[DOI]

Carl Gold

Alex Holub

Neural Networks, 2005

2004

Using the Equivalent Kernel to Understand Gaussian Process Regression.

[BibT_eX]

[DOI]

Christopher K. I. Williams

Proceedings of the Advances in Neural Information Processing Systems 17 [Neural Information Processing Systems, 2004

Understanding Gaussian Process Regression Using the Equivalent Kernel.

[BibT_eX]

[DOI]

Christopher K. I. Williams

Proceedings of the Deterministic and Statistical Methods in Machine Learning, 2004

Can Gaussian Process Regression Be Made Robust Against Model Mismatch?

[BibT_eX]

[DOI]

Proceedings of the Deterministic and Statistical Methods in Machine Learning, 2004

2003

Model selection for support vector machine classification.

[BibT_eX]

[DOI]

Carl Gold

Neurocomputing, 2003

2002

Learning Curves for Gaussian Process Regression: Approximations and Bounds.

[BibT_eX]

[DOI]

Anason S. Halees

Neural Comput., 2002

Bayesian Methods for Support Vector Machines: Evidence and Predictive Class Probabilities.

[BibT_eX]

[DOI]

Mach. Learn., 2002

2001

Gaussian Process Regression with Mismatched Models.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 14 [Neural Information Processing Systems: Natural and Synthetic, 2001

1999

Probabilistic Methods for Support Vector Machines.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 12, [NIPS Conference, Denver, Colorado, USA, November 29, 1999

Gaussian Fields for Approximate Inference in Layered Sigmoid Belief Networks.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 12, [NIPS Conference, Denver, Colorado, USA, November 29, 1999

1998

Online Learning from Finite Training Sets and Robustness to Input Bias.

[BibT_eX]

[DOI]

Neural Comput., 1998

Learning Curves for Gaussian Processes.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 11, [NIPS Conference, Denver, Colorado, USA, November 30, 1998

On-Line Learning with Restricted Training Sets: Exact Solution as Benchmark for General Theories.

[BibT_eX]

[DOI]

H. C. Rae

Anthony C. C. Coolen

Proceedings of the Advances in Neural Information Processing Systems 11, [NIPS Conference, Denver, Colorado, USA, November 30, 1998

1997

On-line Learning from Finite Training Sets in Nonlinear Networks.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 10, 1997

1996

Online Learning from Finite Training Sets: An Analytical Case Study.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 9, 1996

1995

Asking intelligent questions : the statistical mechanics of query learning.

[BibT_eX]

[DOI]

PhD thesis, 1995

Test Error Fluctuations in Finite Linear Perceptrons.

[BibT_eX]

[DOI]

David Saad

Neural Comput., 1995

Learning with ensembles: How overfitting can be useful.

[BibT_eX]

[DOI]

Anders Krogh

Proceedings of the Advances in Neural Information Processing Systems 8, 1995

Minimum entropy queries for linear students learning nonlinear rules.

[BibT_eX]

[DOI]

Proceedings of the 3rd European Symposium on Artificial Neural Networks, 1995

1994

Learning from queries for maximum information gain in imperfectly learnable problems.

[BibT_eX]

[DOI]

David Saad

Proceedings of the Advances in Neural Information Processing Systems 7, 1994

Learning in large linear perceptrons and why the thermodynamic limit is relevant to the real world.

[BibT_eX]

[DOI]