Peter Sollich

2005
18	EE	Carl 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
17	EE	Peter Sollich: Can Gaussian Process Regression Be Made Robust Against Model Mismatch? Deterministic and Statistical Methods in Machine Learning 2004: 199-210
16	EE	Peter Sollich, Christopher K. I. Williams: Understanding Gaussian Process Regression Using the Equivalent Kernel. Deterministic and Statistical Methods in Machine Learning 2004: 211-228
15	EE	Peter Sollich, Christopher K. I. Williams: Using the Equivalent Kernel to Understand Gaussian Process Regression. NIPS 2004
2003
14	EE	Carl 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)
12	EE	Peter Sollich, Anason Halees: Learning Curves for Gaussian Process Regression: Approximations and Bounds. Neural Computation 14(6): 1393-1428 (2002)
2001
11	EE	Peter Sollich: Gaussian Process Regression with Mismatched Models. NIPS 2001: 519-526
1999
10	EE	Peter Sollich: Probabilistic Methods for Support Vector Machines. NIPS 1999: 349-355
9	EE	David Barber, Peter Sollich: Gaussian Fields for Approximate Inference in Layered Sigmoid Belief Networks. NIPS 1999: 393-399
1998
8	EE	H. 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
7	EE	Peter 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
4	EE	Peter Sollich, David Barber: Online Learning from Finite Training Sets: An Analytical Case Study. NIPS 1996: 274-280
1995
3	EE	Peter Sollich, Anders Krogh: Learning with ensembles: How overfitting can be useful. NIPS 1995: 190-196
1994
2	EE	Peter Sollich: Learning in large linear perceptrons and why the thermodynamic limit is relevant to the real world. NIPS 1994: 207-214
1	EE	Peter Sollich, David Saad: Learning from queries for maximum information gain in imperfectly learnable problems. NIPS 1994: 287-294

Coauthor Index

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

Colors in the list of coauthors