Bumptrees for Efficient Function, Constraint and Classification Learning

A new class of data structures called "bumptrees" is described. These structures are useful for efficiently implementing a number of neural network related operations. An empirical comparison with radial basis functions is presented on a robot arm mapping learning task. Applications to density estimation, classification, and constraint representation and learning are also outlined. 1 WHAT IS A BUMPTREE? A bumptree is a new geometric data structure which is useful for efficiently learning, representing, and evaluating geometric relationships in a variety of contexts. They are a natural generalization of several hierarchical geometric data structures including oct-trees, k-d trees, balltrees and boxtrees. They are useful for many geometric learning tasks including approximating functions, constraint surfaces, classification regions, and probability densities from samples. In the function approximation case, the approach is related to radial basis function neural networks, but ...

• Type: Conference
• Year: 1990
• Where: NIPS
• Authors: Stephen M. Omohundro
• Keywords: Data Structures, Geometric Data Structures, NIPS 1990, NIPS 2007, Radial Basis Functions