Browsing AURA by Author "Oommen, John"
Now showing items 41-60 of 65
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On the analysis of a random walk-jump chain with tree-based transitions and its applications to faulty dichotomous search
Yazidi, Anis; Oommen, John (Journal article; Peer reviewed, 2018) -
On the Classification of Dynamical Data Streams Using Novel “Anti–Bayesian” Techniques
Hammer, Hugo Lewi; Yazidi, Anis; Oommen, John (Journal article; Peer reviewed, 2018) -
On the Cryptanalysis of Two Cryptographic Algorithms That Utilize Chaotic Neural Networks
Qin, Ke; Oommen, John (Journal article; Peer reviewed, 2015) -
On the Foundations of Multinomial Sequence Based Estimation
Oommen, John; Kim, Sang-Woon (Chapter, 2016) -
On the Online Classification of Data Streams Using Weak Estimators
Tavasoli, Hanane; Oommen, John; Yazidi, Anis (Chapter, 2016) -
On using "Stochastic learning on the line" to design novel distance estimation methods
Havelock, Jessica; Oommen, John; Granmo, Ole-Christoffer (Journal article; Peer reviewed, 2018) -
On Using Novel "Anti-Bayesian" Techniques for the Classification of Dynamical Data Streams
Hammer, Hugo Lewi; Yazidi, Anis; Oommen, John (Chapter; Peer reviewed, 2017) -
On utilizing an enhanced object partitioning scheme to optimize self-organizing lists-on-lists
Bisong, O. Ekaba; Oommen, John (Journal article; Peer reviewed, 2020) -
Optimization channel selection for cognitive radio networks using a distributed Bayesian learning automata-based approach
Lei, Jiao; Zhang, Xuan; Oommen, John; Granmo, Ole-Christoffer (Journal article; Peer reviewed, 2015) -
Optimizing Self-organizing Lists-on-Lists Using Transitivity and Pursuit-Enhanced Object Partitioning
Bisong, O. Ekaba; Oommen, John (IFIP Advances in Information and Communication Technology; vol. 583, Chapter; Peer reviewed, 2020) -
Particle Field Optimization: A New Paradigm for Swarm Intelligence
Bell, Nathan; Oommen, John (Chapter; Peer reviewed, 2015) -
Pattern classification using a new border identification paradigm: The nearest border technique
Li, Yifeng; Oommen, John; Ngom, Alioune; Rueda, Luis (Journal article; Peer reviewed, 2015) -
Pattern Recognition using the TTOCONROT
Astudillo, César A.; Oommen, John (Chapter; Peer reviewed, 2015) -
Pioneering approaches for enhancing the speed of hierarchical LA by ordering the actions
Omslandseter, Rebekka Olsson; Lei, Jiao; Oommen, John (Peer reviewed; Journal article, 2023)Fixed Structure Stocastic Automata (FSSA), Variable Structure Learning Automata (VSSA), and their discretized versions have been significantly improved by utilizing inexpensive estimates of the actions' reward probabilities. ... -
Solving Stochastic Root-Finding with adaptive d-ary search
Yazidi, Anis; Oommen, John (Chapter; Peer reviewed, 2015) -
Solving Two-Person Zero-Sum Stochastic Games With Incomplete Information Using Learning Automata With Artificial Barriers
Yazidi, Anis; Silvestre, Daniel; Oommen, John (Journal article; Peer reviewed, 2021)Learning automata (LA) with artificially absorbing barriers was a completely new horizon of research in the 1980s (Oommen, 1986). These new machines yielded properties that were previously unknown. More recently, absorbing ... -
Space and depth-related enhancements of the history-ADS strategy in game playing
Polk, Spencer; Oommen, John (Chapter; Peer reviewed, 2015) -
Stochastic discretized learning-based weak estimation: a novel estimation method for non-stationary environments
Yazidi, Anis; Oommen, John; Horn, Geir Henrik; Granmo, Ole-Christoffer (Journal article; Peer reviewed, 2016) -
Text Classification Using Novel “Anti-Bayesian” Techniques
Oommen, John; Khoury, Richard; Schmidt, Aron (Chapter; Peer reviewed, 2015) -
Text Classification Using “Anti”-Bayesian Quantile Statistics-Based Classifiers
Oommen, John; Khoury, Richard; Schmidt, Aron (Peer reviewed; Journal article, 2016)The problem of Text Classification (TC) has been studied for decades, and this problem is particularly interesting because the features are derived from syntactic or semantic indicators, while the classification, in and ...