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Peter Sollich

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2005
18EECarl Gold, Alex Holub, Peter Sollich: Bayesian approach to feature selection and parameter tuning for support vector machine classifiers. Neural Networks 18(5-6): 693-701 (2005)
2004
17EEPeter Sollich: Can Gaussian Process Regression Be Made Robust Against Model Mismatch? Deterministic and Statistical Methods in Machine Learning 2004: 199-210
16EEPeter Sollich, Christopher K. I. Williams: Understanding Gaussian Process Regression Using the Equivalent Kernel. Deterministic and Statistical Methods in Machine Learning 2004: 211-228
15EEPeter Sollich, Christopher K. I. Williams: Using the Equivalent Kernel to Understand Gaussian Process Regression. NIPS 2004
2003
14EECarl Gold, Peter Sollich: Model selection for support vector machine classification. Neurocomputing 55(1-2): 221-249 (2003)
2002
13 Peter Sollich: Bayesian Methods for Support Vector Machines: Evidence and Predictive Class Probabilities. Machine Learning 46(1-3): 21-52 (2002)
12EEPeter Sollich, Anason Halees: Learning Curves for Gaussian Process Regression: Approximations and Bounds. Neural Computation 14(6): 1393-1428 (2002)
2001
11EEPeter Sollich: Gaussian Process Regression with Mismatched Models. NIPS 2001: 519-526
1999
10EEPeter Sollich: Probabilistic Methods for Support Vector Machines. NIPS 1999: 349-355
9EEDavid Barber, Peter Sollich: Gaussian Fields for Approximate Inference in Layered Sigmoid Belief Networks. NIPS 1999: 393-399
1998
8EEH. C. Rae, Peter Sollich, Anthony C. C. Coolen: On-Line Learning with Restricted Training Sets: Exact Solution as Benchmark for General Theories. NIPS 1998: 316-322
7EEPeter Sollich: Learning Curves for Gaussian Processes. NIPS 1998: 344-350
6 Peter Sollich, David Barber: Online Learning from Finite Training Sets and Robustness to Input Bias. Neural Computation 10(8): 2201-2217 (1998)
1997
5 Peter Sollich, David Barber: On-line Learning from Finite Training Sets in Nonlinear Networks. NIPS 1997
1996
4EEPeter Sollich, David Barber: Online Learning from Finite Training Sets: An Analytical Case Study. NIPS 1996: 274-280
1995
3EEPeter Sollich, Anders Krogh: Learning with ensembles: How overfitting can be useful. NIPS 1995: 190-196
1994
2EEPeter Sollich: Learning in large linear perceptrons and why the thermodynamic limit is relevant to the real world. NIPS 1994: 207-214
1EEPeter Sollich, David Saad: Learning from queries for maximum information gain in imperfectly learnable problems. NIPS 1994: 287-294

Coauthor Index

1David Barber [4] [5] [6] [9]
2Anthony C. C. Coolen [8]
3Carl Gold [14] [18]
4Anason Halees [12]
5Alex Holub [18]
6Anders Krogh [3]
7H. C. Rae [8]
8David Saad [1]
9Christopher K. I. Williams [15] [16]

Colors in the list of coauthors

Copyright © Sun May 17 03:24:02 2009 by Michael Ley (ley@uni-trier.de)