Browsing AURA by Author "Oommen, John"
Now showing items 1-20 of 65
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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 formal proof of the e-optimality of discretized pursuit algorithms
Zhang, Xuan; Oommen, John; Granmo, Ole-Christoffer; Lei, Jiao (Journal article; Peer reviewed, 2015) -
A Learning Automaton-based Scheme for Scheduling Domestic Shiftable Loads in Smart Grids
Thapa, Rajan; Lei, Jiao; Oommen, John; Yazidi, Anis (Journal article; Peer reviewed, 2017)In this paper, we consider the problem of scheduling shiftable loads, over multiple users, in smart electrical grids. We approach the problem, which is becoming increasingly pertinent in our present energy-thirsty society, ... -
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 strategy for solving the stochastic point location problem using a hierarchical searching scheme
Yazidi, Anis; Granmo, Ole-Christoffer; Oommen, John; Goodwin, Morten (Journal article, 2014)Stochastic point location (SPL) deals with the problem of a learning mechanism (LM) determining the optimal point on the line when the only input it receives are stochastic signals about the direction in which it should ... -
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) -
“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, ...