SUPANOVA: a sparse, transparent modelling approach
S. Richard Gunn, Martin Brown · 2003
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.