Application of a general learning algorithm to the control of robotic manipulators
International Journal of Robotics Research
Radial basis functions for multivariable interpolation: a review
Algorithms for approximation
“Fast learning in multi-resolution hierarchies”
Advances in neural information processing systems 1
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Multidimensional binary search trees used for associative searching
Communications of the ACM
Task Decomposition Through Competition in a Modular Connectionist
Task Decomposition Through Competition in a Modular Connectionist
Dimensionality Estimation, Manifold Learning and Function Approximation using Tensor Voting
The Journal of Machine Learning Research
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I describe a new algorithm for approximating continuous functions in high-dimensional input spaces. The algorithm builds a tree-structured network of variable size, which is determined both by the distribution of the input data and by the function to be approximated. Unlike other tree-structured algorithms, learning occurs through completely local mechanisms and the weights and structure are modified incrementally as data arrives. Efficient computation in the tree structure takes advantage of the potential for low-order dependencies between the output and the individual dimensions of the input. This algorithm is related to the ideas behind k-d trees (Bentley 1975), CART (Breiman et al. 1984), and MARS (Friedman 1988). I present an example that predicts future values of the Mackey-Glass differential delay equation.