82 research outputs found
Multi-Instance Learning Models for Automated Support of Analysts in Simulated Surveillance Environments
New generations of surveillance drones are being outfitted with numerous high definition cameras. The rapid proliferation of fielded sensors and supporting capacity for processing and displaying data will translate into ever more capable platforms, but with increased capability comes increased complexity and scale that may diminish the usefulness of such platforms to human operators. We investigate methods for alleviating strain on analysts by automatically retrieving content specific to their current task using a machine learning technique known as Multi-Instance Learning (MIL). We use MIL to create a real time model of the analysts' task and subsequently use the model to dynamically retrieve relevant content. This paper presents results from a pilot experiment in which a computer agent is assigned analyst tasks such as identifying caravanning vehicles in a simulated vehicle traffic environment. We compare agent performance between MIL aided trials and unaided trials
Empirically Measuring Transfer Distance for System Design and Operation
Classical machine learning approaches are sensitive to non-stationarity.
Transfer learning can address non-stationarity by sharing knowledge from one
system to another, however, in areas like machine prognostics and defense, data
is fundamentally limited. Therefore, transfer learning algorithms have little,
if any, examples from which to learn. Herein, we suggest that these constraints
on algorithmic learning can be addressed by systems engineering. We formally
define transfer distance in general terms and demonstrate its use in
empirically quantifying the transferability of models. We consider the use of
transfer distance in the design of machine rebuild procedures to allow for
transferable prognostic models. We also consider the use of transfer distance
in predicting operational performance in computer vision. Practitioners can use
the presented methodology to design and operate systems with consideration for
the learning theoretic challenges faced by component learning systems
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