Output details
13 - Electrical and Electronic Engineering, Metallurgy and Materials
University of Southampton
Article title
Probability density estimation with tunable kernels using orthogonal forward regression
Type
D - Journal article
Title of journal
IEEE Transactions on Systems, Man, and Cybernetics, Part B
Article number
-
Volume number
40
Issue number
4
First page of article
1101
ISSN of journal
1083-4419
Year of publication
2010
Number of additional authors
2
Additional information
Significance of output:
This paper utilises a novel orthogonal forward regression technique to generate highly efficient and sparse estimates of the underlying probability density function of an unknown dynamic process from observed data alone. Such an approach is fundamental to understanding nonlinear statistical processes, such as cancer genetics. The algorithm developed in this work is currently being applied by the European Institute of Cancer into prostate cancer diagnosis via the PCA3 gene.
Interdisciplinary
-
Cross-referral requested
-
Research group
None
Proposed double-weighted
No
Double-weighted statement
-
Reserve for a double-weighted output
No
Non-English
No
English abstract
-
