The design of absorbing Bayesian pursuit algorithms and the formal analyses of their \(\varepsilon\)-optimality

DOI10.1007/s10044-016-0535-1zbMath1425.68361OpenAlexW2309123164WikidataQ60327694 ScholiaQ60327694MaRDI QIDQ2337586

B. John Oommen, Xuan Zhang, Ole-Christoffer Granmo

Publication date: 20 November 2019

Published in: PAA. Pattern Analysis and Applications (Search for Journal in Brave)

Full work available at URL: http://hdl.handle.net/11250/2436428

zbMATH Keywords

\(\varepsilon\)-optimality of LA\textit{beta} distributionBayesian pursuit algorithms (BPA)discretized Bayesian pursuit algorithms (DBPA)

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Formal languages and automata (68Q45)

Related Items (1)

Pioneering approaches for enhancing the speed of hierarchical LA by \textit{ordering} the actions

Cites Work

• On ε-Optimality of the Pursuit Learning Algorithm
• Discretized pursuit learning automata
• Absorbing and Ergodic Discretized Two-Action Learning Automata
• Graph partitioning using learning automata
• Learning Automata - A Survey

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