Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
A resource-allocating network for function interpolation
Neural Computation
Hierarchical mixtures of experts and the EM algorithm
Neural Computation
Active learning for vision-based robot grasping
Machine Learning - Special issue on robot learning
Artificial Intelligence Review - Special issue on lazy learning
Boosted mixture of experts: an ensemble learning scheme
Neural Computation
Kalman Filtering and Neural Networks
Kalman Filtering and Neural Networks
Introduction to Robotics: Mechanics and Control
Introduction to Robotics: Mechanics and Control
Machine Learning
Q2: Memory-Based Active Learning for Optimizing Noisy Continuous Functions
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Multi-level partition of unity implicits
ACM SIGGRAPH 2003 Papers
Neural Computation
Neural Computation
Improved GAP-RBF network for classification problems
Neurocomputing
Letters: Convex incremental extreme learning machine
Neurocomputing
Active learning with statistical models
Journal of Artificial Intelligence Research
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Heuristic selection of actions in multiagent reinforcement learning
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Neurocomputing
The cog project: building a humanoid robot
Computation for metaphors, analogy, and agents
IEEE Transactions on Neural Networks
Statistical active learning in multilayer perceptrons
IEEE Transactions on Neural Networks
Multiscale approximation with hierarchical radial basis functions networks
IEEE Transactions on Neural Networks
A generalized growing and pruning RBF (GGAP-RBF) neural network for function approximation
IEEE Transactions on Neural Networks
Universal approximation using incremental constructive feedforward networks with random hidden nodes
IEEE Transactions on Neural Networks
A Fast and Accurate Online Sequential Learning Algorithm for Feedforward Networks
IEEE Transactions on Neural Networks
Implementation of self-organizing neural networks for visuo-motor control of an industrial robot
IEEE Transactions on Neural Networks
HAIS '09 Proceedings of the 4th International Conference on Hybrid Artificial Intelligence Systems
A hierarchical RBF online learning algorithm for real-time 3-D scanner
IEEE Transactions on Neural Networks
Discriminative sample selection for statistical machine translation
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Learning robotic hand-eye coordination through a developmental constraint driven approach
International Journal of Automation and Computing
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In this paper, we describe a new error-driven active learning approach to self-growing radial basis function networks for early robot learning. There are several mappings that need to be set up for an autonomous robot system for sensorimotor coordination and transformation of sensory information from one modality to another, and these mappings are usually highly nonlinear. Traditional passive learning approaches usually cause both large mapping errors and nonuniform mapping error distribution compared to active learning. A hierarchical clustering technique is introduced to group large mapping errors and these error clusters drive the system to actively explore details of these clusters. Higher level local growing radial basis function subnetworks are used to approximate the residual errors from previous mapping levels. Plastic radial basis function networks construct the substrate of the learning system and a simplified node-decoupled extended Kalman filter algorithm is presented to train these radial basis function networks. Experimental results are given to compare the performance among active learning with hierarchical adaptive RBF networks, passive learning with adaptive RBF networks and hierarchical mixtures of experts, as well as their robustness under noise conditions.