9,491 research outputs found
Active Classification: Theory and Application to Underwater Inspection
We discuss the problem in which an autonomous vehicle must classify an object
based on multiple views. We focus on the active classification setting, where
the vehicle controls which views to select to best perform the classification.
The problem is formulated as an extension to Bayesian active learning, and we
show connections to recent theoretical guarantees in this area. We formally
analyze the benefit of acting adaptively as new information becomes available.
The analysis leads to a probabilistic algorithm for determining the best views
to observe based on information theoretic costs. We validate our approach in
two ways, both related to underwater inspection: 3D polyhedra recognition in
synthetic depth maps and ship hull inspection with imaging sonar. These tasks
encompass both the planning and recognition aspects of the active
classification problem. The results demonstrate that actively planning for
informative views can reduce the number of necessary views by up to 80% when
compared to passive methods.Comment: 16 page
A Nonparametric Conjugate Prior Distribution for the Maximizing Argument of a Noisy Function
We propose a novel Bayesian approach to solve stochastic optimization
problems that involve finding extrema of noisy, nonlinear functions. Previous
work has focused on representing possible functions explicitly, which leads to
a two-step procedure of first, doing inference over the function space and
second, finding the extrema of these functions. Here we skip the representation
step and directly model the distribution over extrema. To this end, we devise a
non-parametric conjugate prior based on a kernel regressor. The resulting
posterior distribution directly captures the uncertainty over the maximum of
the unknown function. We illustrate the effectiveness of our model by
optimizing a noisy, high-dimensional, non-convex objective function.Comment: 9 pages, 5 figure
- …