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A massively parallel architecture for a self-organizing neural pattern recognition machine
Computer Vision, Graphics, and Image Processing
Model-based control of a robot manipulator
Model-based control of a robot manipulator
Radial basis functions for multivariable interpolation: a review
Algorithms for approximation
The cascade-correlation learning architecture
Advances in neural information processing systems 2
Using local models to control movement
Advances in neural information processing systems 2
A resource-allocating network for function interpolation
Neural Computation
The computational brain
Neural networks and the bias/variance dilemma
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Neural Networks
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Neural Computation
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Dynamic cell structure learns perfectly topology preserving map
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The handbook of brain theory and neural networks
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Regularization in the selection of radial basis function centers
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IEEE Transactions on Neural Networks
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ICANN '01 Proceedings of the International Conference on Artificial Neural Networks
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IDA '01 Proceedings of the 4th International Conference on Advances in Intelligent Data Analysis
MICRONEURO '99 Proceedings of the 7th International Conference on Microelectronics for Neural, Fuzzy and Bio-Inspired Systems
Incremental Online Learning in High Dimensions
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AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Dimensionality Estimation, Manifold Learning and Function Approximation using Tensor Voting
The Journal of Machine Learning Research
Incremental learning of spatio-temporal patterns with model selection
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Covariate shift and incremental learning
ICONIP'08 Proceedings of the 15th international conference on Advances in neuro-information processing - Volume Part I
A comparative study: function approximation with LWPR and XCSF
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Bayesian robot system identification with input and output noise
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Task-specific generalization of discrete and periodic dynamic movement primitives
IEEE Transactions on Robotics
Learning Non-linear Multivariate Dynamics of Motion in Robotic Manipulators
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A Generalized Path Integral Control Approach to Reinforcement Learning
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Online incremental learning of inverse dynamics incorporating prior knowledge
AIS'11 Proceedings of the Second international conference on Autonomous and intelligent systems
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ICONIP'06 Proceedings of the 13th international conference on Neural Information Processing - Volume Part II
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A biomimetic reach and grasp approach for mechanical hands
Robotics and Autonomous Systems
Proceedings of the 14th annual conference on Genetic and evolutionary computation
On-line motion synthesis and adaptation using a trajectory database
Robotics and Autonomous Systems
Dynamical movement primitives: Learning attractor models for motor behaviors
Neural Computation
From dynamic movement primitives to associative skill memories
Robotics and Autonomous Systems
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We introduce a constructive, incremental learning system for regression problems that models data by means of spatially localized linear models. In contrast to other approaches, the size and shape of the receptive field of each locally linear model, as well as the parameters of the locally linear model itself, are learned independently, that is, without the need for competition or any other kind of communication. Independent learning is accomplished by incrementally minimizing a weighted local cross-validation error. As a result, we obtain a learning system that can allocate resources as needed while dealing with the bias-variance dilemma in a principled way. The spatial localization of the linear models increases robustness toward negative interference. Our learning system can be interpreted as a nonparametric adaptive bandwidth smoother, as a mixture of experts where the experts are trained in isolation, and as a learning system that profits from combining independent expert knowledge on the same problem. This article illustrates the potential learning capabilities of purely local learning and offers an interesting and powerful approach to learning with receptive fields.