Scalable Techniques from Nonparametric Statistics for Real Time Robot Learning
Applied Intelligence
Classifiers that approximate functions
Natural Computing: an international journal
Locally Weighted Projection Regression: Incremental Real Time Learning in High Dimensional Space
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Incremental Online Learning in High Dimensions
Neural Computation
Constructive Incremental Learning from Only Local Information
Neural Computation
Prediction update algorithms for XCSF: RLS, Kalman filter, and gain adaptation
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Classifier fitness based on accuracy
Evolutionary Computation
Resource management and scalability of the XCSF learning classifier system
Theoretical Computer Science
IEEE Transactions on Evolutionary Computation
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Modularization of xcsf for multiple output dimensions
Proceedings of the 13th annual conference on Genetic and evolutionary computation
XCSF with local deletion: preventing detrimental forgetting
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
On-line regression algorithms for learning mechanical models of robots: A survey
Robotics and Autonomous Systems
Learning local linear Jacobians for flexible and adaptive robot arm control
Genetic Programming and Evolvable Machines
Filtering sensory information with XCSF: improving learning robustness and control performance
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Proceedings of the 14th annual conference on Genetic and evolutionary computation
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Function approximation is an important tool that is frequently used in numerical mathematics and engineering. The most challenging approximation problems arise, when even the function class is unknown and the surface has to be approximated online from incoming samples. One way to achieve good approximations of complex non-linear functions is to cluster the input space into small patches, apply linear models in each niche, and recombine these models via a weighted sum. While it is rather simple to optimally fit a linear model to given data, it is fairly complex to find a reasonable structuring of the input space in order to exploit linearities in the underlying function. We compare two algorithms that are able to approximate multi-dimensional, non-linear functions online. The XCSF Learning Classifier System is a modified version of XCS, which is a genetics-based machine learning algorithm. Locally Weighted Projection Regression (LWPR) is a statistics-based machine learning technique that is widely used for function approximation, particularly in robotics. The two algorithms are compared on three benchmark functions by monitoring several performance related measures over the learning trials. Moreover, an illustration of the final input space structuring sheds light on the clustering capabilities.