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DSMML
2004
Springer
13 years 11 months ago
Understanding Gaussian Process Regression Using the Equivalent Kernel
The equivalent kernel [1] is a way of understanding how Gaussian process regression works for large sample sizes based on a continuum limit. In this paper we show how to approximat...
Peter Sollich, Christopher K. I. Williams
DSMML
2004
Springer
13 years 11 months ago
Object Recognition via Local Patch Labelling
Abstract. In recent years the problem of object recognition has received considerable attention from both the machine learning and computer vision communities. The key challenge of...
Christopher M. Bishop, Ilkay Ulusoy
DSMML
2004
Springer
13 years 11 months ago
Can Gaussian Process Regression Be Made Robust Against Model Mismatch?
Learning curves for Gaussian process (GP) regression can be strongly affected by a mismatch between the ‘student’ model and the ‘teacher’ (true data generation process), e...
Peter Sollich
DSMML
2004
Springer
13 years 11 months ago
Integrating Binding Site Predictions Using Non-linear Classification Methods
Yi Sun, Mark Robinson, Rod Adams, Paul Kaye, Alist...
DSMML
2004
Springer
13 years 11 months ago
Redundant Bit Vectors for Quickly Searching High-Dimensional Regions
Applications such as audio fingerprinting require search in high dimensions: find an item in a database that is similar to a query. An important property of this search task is t...
Jonathan Goldstein, John C. Platt, Christopher J. ...
Machine Learning
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