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The system has been evaluated in the domain of table-top game playing and has been shown to be successful at learning protocol behaviours in such real-world audio-visual environments.
Using the same semi-on-line learning protocol as before, a linear regression from sound pressure level and modulation depth to gain was performed.
We developed a 'push-pull' perceptual learning protocol that simultaneously affects the excitatory and inhibitory networks to reduce sensory eye dominance and improve stereopsis in adults with otherwise normal vision [4].
Without using any global information, a fully distributed adaptive iterative learning protocol with distributed adaptive learning laws for the control gains is designed in this paper, under which consensus is reached for all undirected connected communication graph.
Results: We present a machine learning protocol for determining membrane-targeting properties achieving 85 90% accuracy in separating binding and non-binding domains within families.
On the other hand, the learning protocol uses Q-learning to build a knowledge base that is used to enhance the performance of the WCM reasoning machine and address the end-to-end goals of the applications directly.
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This paper presents a cognitive vision system capable of autonomously learning protocols from perceptual observations of dynamic scenes.
Simulation results are finally presented to illustrate the performance and effectiveness of our iterative learning protocols.
This study aimed to design learning protocols for newly born guppies based on knowledge of their social needs.
Additionally, within many of these disciplines, low level programming knowledge is not abundant and scientist are often restricted to predefined machine learning protocols, wrapped in some graphical environment.
Distributed iterative learning protocols guarantee that all follower agents can achieve perfect tracking consensus for both fixed and switching communication topologies, respectively.
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