Retraining and redundancy elimination for a Condensed Nearest Neighbour network

Barschdorff, D.; Bothe, Achim; Gärtner, Ulrich; Jäger, Andreas

doi:10.1007/bfb0024976

Cited by 1 publication

(1 citation statement)

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“…Thus we propose to adapt the database (memory) in order to span the nonlinear parts with more samples, providing a variable sample distribution and a variable size of the local neighborhood. This approach is similar to the socalled condensed nearest neighbor classification [12], which tries to find and retain only those samples that lie on the decision boundaries between classification categories [13]. This results in a piecewise constant approximation of the function with values between the decision boundaries being interpolated as constant.…”

Section: A Memory Managementmentioning

confidence: 99%

Imitation learning with non-parametric regression

Vaandrager¹,

Babuška

Buşoniu

et al. 2012

Proceedings of 2012 IEEE International Conference on Automation, Quality and Testing, Robotics

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Abstract-Humans are very fast learners. Yet, we rarely learn a task completely from scratch. Instead, we usually start with a rough approximation of the desired behavior and take the learning from there. In this paper, we use imitation to quickly generate a rough solution to a robotic task from demonstrations, supplied as a collection of state-space trajectories. Appropriate control actions needed to steer the system along the trajectories are then automatically learned in the form of a (nonlinear) statefeedback control law. The learning scheme has two components: a dynamic reference model and an adaptive inverse process model, both based on a data-driven, non-parametric method called local linear regression. The reference model infers the desired behavior from the demonstration trajectories, while the inverse process model provides the control actions to achieve this behavior and is improved online using learning. Experimental results with a pendulum swing-up problem and a robotic arm demonstrate the practical usefulness of this approach. The resulting learned dynamics are not limited to single trajectories, but capture instead the overall dynamics of the motion, making the proposed approach a promising step towards versatile learning machines such as future household robots, or robots for autonomous missions.

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Section: A Memory Managementmentioning

confidence: 99%