Title :
SUPANOVA: a sparse, transparent modelling approach
Author :
Gunn, Steve R. ; Brown, Martin
Author_Institution :
Dept. of Electron. & Comput. Sci., Southampton Univ., UK
Abstract :
Traditional neural networks produce opaque models that are difficult to interpret. This work describes a transparent, non-linear, modelling approach that enables the constructed models to be visualised, enhancing their validation and interpretation. The technique combines the representational advantage of a sparse ANOVA decomposition, with the good generalisation ability of a support vector machine
Keywords :
Hilbert spaces; modelling; neural nets; splines (mathematics); statistical analysis; SUPANOVA; generalisation ability; model interpretation; model validation; sparse ANOVA decomposition; sparse transparent modelling approach; support vector machine; transparent nonlinear modelling approach; Analysis of variance; Computer science; Gunn devices; Hilbert space; Intersymbol interference; Kernel; Neural networks; Sampling methods; Support vector machines; Visualization;
Conference_Titel :
Neural Networks for Signal Processing IX, 1999. Proceedings of the 1999 IEEE Signal Processing Society Workshop.
Conference_Location :
Madison, WI
Print_ISBN :
0-7803-5673-X
DOI :
10.1109/NNSP.1999.788119
