Browsing Scientific Publications in Information and Communication Technology by Author "Oommen, John"
Now showing items 1-20 of 60
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A Cluster Analysis of Stock Market Data Using Hierarchical SOMs
Astudillo, César A.; Poblete, Jorge; Resta, Marina; Oommen, John (Chapter; Peer reviewed, 2016)The analysis of stock markets has become relevant mainly because of its financial implications. In this paper, we propose a novel methodology for performing a structured cluster analysis of stock market data. Our proposed ... -
A novel abstraction for swarm intelligence: particle field optimization
Bell, Nathan; Oommen, John (Peer reviewed; Journal article, 2016)Particle swarm optimization (PSO) is a popular meta-heuristic for black-box optimization. In essence, within this paradigm, the system is fully defined by a swarm of “particles” each characterized by a set of features such ... -
A Novel Clustering Algorithm based on a Non-parametric "Anti-Bayesian" Paradigm
Hammer, Hugo Lewi; Yazidi, Anis; Oommen, John (Chapter; Peer reviewed, 2015) -
A novel technique for stochastic root-finding: Enhancing the search with adaptive d-ary search
Yazidi, Anis; Oommen, John (Peer reviewed; Journal article, 2017)The most fundamental problem encountered in the field of stochastic optimization and control, is the Stochastic Root Finding (SRF) problem where the task is to locate (or in the context of control, to move towards), an ... -
Achieving Intelligent Traffic-aware Consolidation of Virtual Machines in a Data Center Using Learning Automata
Jobava, Akaki; Yazidi, Anis; Oommen, John; Begnum, Kyrre (Chapter; Peer reviewed, 2016) -
“Anti-Bayesian” flat and hierarchical clustering using symmetric quantiloids
Hammer, Hugo Lewi; Yazidi, Anis; Oommen, John (Journal article; Peer reviewed, 2017)A Pattern Recognition (PR) system that does not involve labelled samples requires the clustering of the samples into their respective classes before the training and testing can be achieved. All of the reported clustering ... -
“Anti-Bayesian” Flat and Hierarchical Clustering Using Symmetric Quantiloids
Yazidi, Anis; Hammer, Hugo Lewi; Oommen, John (Chapter; Peer reviewed, 2016) -
Challenging Established Move Ordering Strategies with Adaptive Data Structures
Polk, Spencer; Oommen, John (Chapter, 2016) -
Concept Drift Detection Using Online Histogram-Based Bayesian Classifiers
Astudillo, César A.; Gonzalez, Javier I.; Oommen, John; Yazidi, Anis (Chapter, 2016)In this paper, we present a novel algorithm that performs online histogram-based classification, i.e., specifically designed for the case when the data is dynamic and its distribution is non-stationary. Our method, called ... -
A Conclusive Analysis of the Finite-Time Behavior of the Discretized Pursuit Learning Automaton
Zhang, Xuan; Jiao, Lei; Oommen, John; Granmo, Ole-Christoffer (Journal article; Peer reviewed, 2019)This paper deals with the finite-time analysis (FTA) of learning automata (LA), which is a topic for which very little work has been reported in the literature. This is as opposed to the asymptotic steady-state analysis ... -
Dynamic Ordering of Firewall Rules Using a Novel Swapping Window-based Paradigm
Mohan, Ratish; Yazidi, Anis; Feng, Boning; Oommen, John (Chapter, 2016) -
Enhancing History-Based Move Ordering in Game Playing Using Adaptive Data Structures
Polk, Spencer; Oommen, John (Chapter, 2015) -
Enhancing the Prediction of Lung Cancer Survival Rates Using 2D Features from 3D Scans
Ghani, Tahira; Oommen, John (Lecture Notes in Computer Science; vol. 12132, Chapter; Peer reviewed, 2020) -
The Hierarchical Continuous Pursuit Learning Automation : A Novel Scheme for Environments With Large Numbers of Actions
Yazidi, Anis; Zhang, Xuan; Lei, Jiao; Oommen, John (Journal article; Peer reviewed, 2019) -
The Hierarchical Discrete Learning Automaton Suitable for Environments with Many Actions and High Accuracy Requirements
Omslandseter, Rebekka Olsson; Jiao, Lei; Zhang, Xuan; Yazidi, Anis; Oommen, John (Peer reviewed; Journal article, 2022)Since its early beginning, the paradigm of Learning Automata (LA), has attracted much interest. Over the last decades, new concepts and various improvements have been introduced to increase the LA’s speed and accuracy, ... -
The Hierarchical Discrete Pursuit Learning Automaton: A Novel Scheme With Fast Convergence and Epsilon-Optimality
Omslandseter, Rebekka Olsson; Jiao, Lei; Zhang, Xuan; Yazidi, Anis; Oommen, John (Peer reviewed; Journal article, 2022)Since the early 1960s, the paradigm of learning automata (LA) has experienced abundant interest. Arguably, it has also served as the foundation for the phenomenon and field of reinforcement learning (RL). Over the decades, ... -
Higher-Fidelity Frugal and Accurate Quantile Estimation Using a Novel Incremental Discretized Paradigm
Yazidi, Anis; Hammer, Hugo Lewi; Oommen, John (Journal article; Peer reviewed, 2018) -
Identifying unreliable sensors without a knowledge of the ground truth in deceptive environments
Yazidi, Anis; Oommen, John; Goodwin, Morten (Journal article; Peer reviewed, 2017)This paper deals with the extremely fascinating area of “fusing” the outputs of sensors without any knowledge of the ground truth. In an earlier paper, the present authors had recently pioneered a solution, by mapping ... -
A Learning-Automata Based Solution for Non-equal Partitioning: Partitions with Common GCD Sizes
Omslandseter, Rebekka Olsson; Jiao, Lei; Oommen, John (Lecture Notes in Computer Science;12799, Peer reviewed; Journal article, 2021)The Object Migration Automata (OMA) has been used as a powerful tool to resolve real-life partitioning problems in random Environments. The virgin OMA has also been enhanced by incorporating the latest strategies in Learning ... -
Multinomial Sequence Based Estimation Using Contiguous Subsequences of Length Three
Oommen, John; Kim, Sang-Woon (Chapter, 2016)