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13 June 2023 Real time machine learning and decision-making with hardware expert systems and gradient descent trained expert systems
Jeremy Straub
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Proceedings Volume PC12528, Real-Time Image Processing and Deep Learning 2023; PC1252806 (2023) https://doi.org/10.1117/12.2670081
Event: SPIE Defense + Commercial Sensing, 2023, Orlando, Florida, United States
Abstract
Three previously proposed machine learning techniques (a technique based on applying gradient descent training to a fractional value expert system, the fractional value expert system technique’s real time hardware implementation, and a Boolean expert system hardware implementation) are evaluated for application to defense-relevant real time processing challenges. These techniques are defensible, meaning that their decisions are constrained by logical pathways that can be reviewed by a human prior to a decision being made and acted upon by the system (as opposed to simply explained afterwards). These techniques and several conventional techniques are evaluated under multiple defense-relevant real time processing scenarios.
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Jeremy Straub "Real time machine learning and decision-making with hardware expert systems and gradient descent trained expert systems", Proc. SPIE PC12528, Real-Time Image Processing and Deep Learning 2023, PC1252806 (13 June 2023); https://doi.org/10.1117/12.2670081
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KEYWORDS
Machine learning
Decision making
Education and training
Artificial intelligence
Robotic systems
Vehicle control
Detection and tracking algorithms
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