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Under these assumptions, we use game theoretic learning setup to make transmitters learn about the power allocation under each state.
His current interests include cognitive radio, information theoretic learning, telecommunication system, parallel software scalability, and parallel algorithms.
CLF is robust to outliers through the mechanism of correntropy which is widely applied in information theoretic learning.
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The new methodology became known as information-theoretic learning (ITL).
This paper presents a formal game-theoretic belief learning approach to RAT.
This paper presents a formal game-theoretic belief learning approach to model criminology's routine activity theory (RAT).
The game-theoretic belief learning approach presented in this paper offers a method to interdict opportunity, deter offenders and protect the public.
This paper introduces an original model for using ABM and game-theoretic belief learning to explore RAT's offender, target, and guardian dynamics and potential displacement behaviors.
This paper presents a new approach to explore this displacement problem using agent-based modeling (ABM) and game-theoretic belief learning to simulate offender (criminal), target (victim), and guardian (law enforcement) behaviors.
We focus on a game-theoretic regression learning setting where private information is elicited from multiple agents with different, possibly conflicting, views on how to label the points of an input space.
These support vectors have several computational and learning theoretic consequences [ 32].
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