Hierarchical mixtures of experts and the EM algorithm
Neural Computation
Multiple paired forward and inverse models for motor control
Neural Networks - Special issue on neural control and robotics: biology and technology
Constructive incremental learning from only local information
Neural Computation
Locally Weighted Projection Regression: Incremental Real Time Learning in High Dimensional Space
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Variational Learning for Switching State-Space Models
Neural Computation
A greedy approach to identification of piecewise affine models
HSCC'03 Proceedings of the 6th international conference on Hybrid systems: computation and control
Locally adaptive classification piloted by uncertainty
ICML '06 Proceedings of the 23rd international conference on Machine learning
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Sensorimotor data from many interesting physical interactions comprises discontinuities. While existing locally weighted learning approaches aim at learning smooth functions, we propose a model that learns how to switch discontinuously between local models. The local responsibilities, usually represented by Gaussian kernels, are learned by a product of local sigmoidal classifiers that can represent complex shaped and sharply bounded regions. Local models are incrementally added. A locality prior constrains them to learn only local data---which is the key ingredient for incremental learning with local models.