The Power of the 'Pursuit' Learning Paradigm in the Partitioning of Data
Autor: | B. John Oommen, Abdolreza Shirvani |
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Přispěvatelé: | School of Computer Science (Carleton, Ottawa), Carleton University, University of Agder (UIA), John MacIntyre, Ilias Maglogiannis, Lazaros Iliadis, Elias Pimenidis, TC 12, WG 12.5 |
Jazyk: | angličtina |
Rok vydání: | 2019 |
Předmět: |
Mathematical optimization
Theoretical computer science Learning automata Basis (linear algebra) Computer science Rank (computer programming) Object Partitioning Partitioning-based learning Estimator Learning Automata 02 engineering and technology Probability vector Field (computer science) Automaton Ranking 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing [INFO]Computer Science [cs] Object Migration Automaton |
Zdroj: | IFIP Advances in Information and Communication Technology 15th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI) 15th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), May 2019, Hersonissos, Greece. pp.3-16, ⟨10.1007/978-3-030-19823-7_1⟩ Engineering Applications of Neural Networks ISBN: 9783030202569 EANN IFIP Advances in Information and Communication Technology ISBN: 9783030198220 AIAI |
DOI: | 10.1007/978-3-030-19823-7_1⟩ |
Popis: | Traditional Learning Automata (LA) work with the understanding that the actions are chosen purely based on the “state” in which the machine is. This modus operandus completely ignores any estimation of the Random Environment’s (RE’s) (specified as \(\mathbb {E}\)) reward/penalty probabilities. To take these into consideration, Estimator/Pursuit LA utilize “cheap” estimates of the Environment’s reward probabilities to make them converge by an order of magnitude faster. This concept is quite simply the following: Inexpensive estimates of the reward probabilities can be used to rank the actions. Thereafter, when the action probability vector has to be updated, it is done not on the basis of the Environment’s response alone, but also based on the ranking of these estimates. While this phenomenon has been utilized in the field of LA, until recently, it has not been incorporated into solutions that solve partitioning problems. In this paper (The second author gratefully acknowledges the partial support of NSERC, the Natural Sciences and Engineering Council of Canada), we will submit a complete survey of how the “Pursuit” learning paradigm can be and has been used in Object Partitioning. The results demonstrate that incorporating this paradigm can hasten the partitioning by a order of magnitude. |
Databáze: | OpenAIRE |
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