|Title: Nested Networks for Robot Control Applications of Neural Networks|
|Written by: Jansen A, Smagt P van der, Groen F|
|on pages: 221--239|
|Editor: A. F. Murray|
|Publisher: Kluwer Academic Publishers|
|Address: Dordrecht, the Netherlands|
Abstract: We present a method for accurate representation of high-dimensional unknown functions from random samples drawn from its input space. The method builds representations of the function by recursively splitting the input space in smaller subspaces. The representations of the function at all levels (i.e., depths in the tree) are retained during the learning process, such that a good generalisation is available as well as more accurate representations in some subareas. Therefore, fast and accurate learning are combined in this method.