B. Oommen

发表

On Terrain Model Acquisition by a Point Robot Amidst Polyhedral Obstacles

Nageswara, S. V.RAO., Iyengar, 1981 .

Pattern recognition of strings with substitutions, insertions, deletions and generalized transpositions

B.J. Oommen, R.K.S. Loke, B. Oommen, 1997, Pattern Recognit..

A common basis for similarity measures involving two strings

R. Kashyap, B. Oommen, 1983 .

Multiaction learning automata possessing ergodicity of the mean

B. John Oommen, Mandayam A. L. Thathachar, M. Thathachar, 1985, Inf. Sci..

The Noisy Substring Matching Problem

B. John Oommen, Rangasami L. Kashyap, R. Kashyap, 1983, IEEE Transactions on Software Engineering.

A formal analysis of why heuristic functions work

B. John Oommen, Luis Rueda, B. Oommen, 2005, Artif. Intell..

On using parametric string distances and vector quantization in designing syntactic pattern recognition systems

B. Oommen, R. Loke, 1997, 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation.

Achieving Unbounded Resolution in Finite Player Goore Games Using Stochastic Automata, and Its Applications

B. Oommen, Ole-Christoffer Granmo, A. Pedersen, 2012 .

Ideal Chaotic Pattern Recognition Using the Modified Adachi Neural Network

B. Oommen, Ke Qin, 2012 .

A Geometrical Approach to Polygonal Dissimilarity and Shape Matching

B. John Oommen, Rangasami L. Kashyap, R. Kashyap, 1982, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Towards a Learning Automata Solution to the Multi-Constraint Partitioning Problem

G. Horn, B.J. Oommen, B. Oommen, 2006, 2006 IEEE Conference on Cybernetics and Intelligent Systems.

Learning Automata-based Solutions to the Optimal Web

B. John Oommen, Ole-Christoffer Granmo, B. Oommen, 2011 .

S TOCHASTIC S EARCHING ON THE L INE AND ITS A PPLICATIONS TO P ARAMETER L EARNING IN N ONLINEAR O PTIMIZATION

B. Oommen, 1995 .

On the analysis of a random walk-jump chain with tree-based transitions and its applications to faulty dichotomous search

B. Oommen, A. Yazidi, 2018 .

Encryption Possessing Statistical Perfect Secrecy and Stealth ∗

B. Oommen, L. Rueda, 2004 .

Stochastic Automata Solutions to the Object Partitioning Problem

B. John Oommen, D. C. Ma, Daniel C. Y. Ma, 1991, Comput. J..

Chaotic Pattern Recognition Using the Adachi Neural Network Modified in a Small-World Way ⋆

B. Oommen, Ke Qin, 2014 .

Moment-Preserving Piecewise Linear Approximations of Signals and Images

B. John Oommen, Thai B. Nguyen, B. Oommen, 1997, IEEE Trans. Pattern Anal. Mach. Intell..

Learning Automata with Artificial Reflecting Barriers in Games with Limited Information

B. Oommen, A. Yazidi, Ismail Hassan, 2022, The International FLAIRS Conference Proceedings.

Absorbing and Ergodic Discretized Two-Action Learning Automata

B. John Oommen, B. Oommen, 1986, IEEE Trans. Syst. Man Cybern..

Using Pattern Recognition Techniques to Derive a Formal Analysis of Why Heuristics Functions Work

B. John Oommen, Luis Rueda, B. Oommen, 2002, PRIS.

Solving Two-Person Zero-Sum Stochastic Games With Incomplete Information Using Learning Automata With Artificial Barriers

B. Oommen, A. Yazidi, D. Silvestre, 2021, IEEE Transactions on Neural Networks and Learning Systems.

AI-Based Syntactic Pattern Recognition of Sequences Inventors

B. Oommen, 2005 .

On Using a Hierarchy of Twofold Resource Allocation Automata to Solve Stochastic Nonlinear Resource Allocation Problems

B. Oommen, Ole-Christoffer Granmo, 2007, Australian Conference on Artificial Intelligence.

Adaptive linear list reorganization under a generalized query system

J. R. Zgierski, B. Oommen, R. Valiveti, 1995, Journal of Applied Probability.

On utilizing dependence-based information to enhance micro-aggregation for secure statistical databases

B. Oommen, Ebaa Fayyoumi, 2013, Pattern Analysis and Applications.

Nonparametric “anti-Bayesian” quantile-based pattern classification

B. Oommen, M. Razmkhah, Fatemeh Mahmoudi, 2020, Pattern Analysis and Applications.

Topology-oriented self-organizing maps: a survey

B. Oommen, C. Astudillo, 2014, Pattern Analysis and Applications.

Stochastic searching on the line and its applications to parameter learning in nonlinear optimization

B. John Oommen, B. Oommen, 1997, IEEE Trans. Syst. Man Cybern. Part B.

Estimation of distributions involving unobservable events: the case of optimal search with unknown Target Distributions

B. John Oommen, Qingxin Zhu, B. Oommen, 2009, Pattern Analysis and Applications.

Higher-Fidelity Frugal and Accurate Quantile Estimation Using a Novel Incremental Discretized Paradigm

Anis Yazidi, B. John Oommen, Hugo Hammer, 2018, IEEE Access.

Nonparametric “anti-Bayesian” quantile-based pattern classification

B. John Oommen, Mostafa Razmkhah, Fatemeh Mahmoudi, 2020, Pattern Analysis and Applications.

Parameter learning from stochastic teachers and stochastic compulsive liars

B.J. Oommen, G. Raghunath, B. Kuipers, 2006, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

On How to Learn from a Stochastic Teacher or a Stochastic Compulsive Liar of Unknown Identity

Benjamin Kuipers, B. John Oommen, Govindachari Raghunath, 2003, Australian Conference on Artificial Intelligence.

Automata learning and intelligent tertiary searching for stochastic point location

B. John Oommen, Govindachari Raghunath, B. Oommen, 1998, IEEE Trans. Syst. Man Cybern. Part B.

Mixture decomposition for distributions from the exponential family using a generalized method of moments

B. John Oommen, S. T. Sum, B. Oommen, 1995, IEEE Trans. Syst. Man Cybern..

String taxonomy using learning automata

B. John Oommen, Edward V. de St. Croix, B. Oommen, 1997, IEEE Trans. Syst. Man Cybern. Part B.

Pattern Recognition using the TTOCONROT

B. John Oommen, César A. Astudillo, B. Oommen, 2015, IEA/AIE.

On achieving semi-supervised pattern recognition by utilizing tree-based SOMs

B. John Oommen, César A. Astudillo, B. Oommen, 2013, Pattern Recognit..

Pattern classification using a new border identification paradigm: The nearest border technique

B. John Oommen, Alioune Ngom, Luis Rueda, 2015, Neurocomputing.

A New Paradigm for Pattern Classification: Nearest Border Techniques

B. John Oommen, Alioune Ngom, Luis Rueda, 2013, Australasian Conference on Artificial Intelligence.

Peptide classification using optimal and information theoretic syntactic modeling

B. John Oommen, Zehra Cataltepe, Eser Aygün, 2010, Pattern Recognit..

On using distribution theory to prove the epsilon-optimality of stubborn learning mechanisms

B. John Oommen, Jens Peter Reus Christensen, J. Christensen, 1989, Conference Proceedings., IEEE International Conference on Systems, Man and Cybernetics.

Self-organizing maps whose topologies can be learned with adaptive binary search trees using conditional rotations pdf

B. John Oommen, César A. Astudillo, B. Oommen, 2015, Pattern Recognit..

On Using Adaptive Binary Search Trees to Enhance Self Organizing Maps

B. John Oommen, César A. Astudillo, B. Oommen, 2009, Australasian Conference on Artificial Intelligence.

Fast BMU Search in SOMs Using Random Hyperplane Trees

B. John Oommen, César A. Astudillo, B. Oommen, 2014, PRICAI.

Fast, efficient and accurate solutions to the Hamiltonian path problem using neural approaches

B. John Oommen, I. K. Altinel, I. Kuban Altinel, 2000, Comput. Oper. Res..

The Kohonen network incorporating explicit statistics and its application to the travelling salesman problem

B. John Oommen, I. K. Altinel, I. Kuban Altinel, 1999, Neural Networks.

Novel Results on Random Walk-Jump Chains That Possess Tree-Based Transitions

Anis Yazidi, B. John Oommen, B. Oommen, 2017, CORES.

On the Analysis of a Random Interleaving Walk–Jump Process with Applications to Testing

Anis Yazidi, B. John Oommen, Ole-Christoffer Granmo, 2011 .

A New Tool for the Modeling of AI and Machine Learning Applications: Random Walk-Jump Processes

Anis Yazidi, B. John Oommen, Ole-Christoffer Granmo, 2011, HAIS.

On the analysis of a new Markov chain which has applications in AI and machine learning

Anis Yazidi, B. John Oommen, Ole-Christoffer Granmo, 2011, 2011 24th Canadian Conference on Electrical and Computer Engineering(CCECE).

Large-scale neuro-modeling for understanding and explaining some brain-related chaotic behavior

B. John Oommen, Doron Nussbaum, Dragos Calitoiu, 2012, Simul..

On enhancing the object migration automaton using the Pursuit paradigm

B. John Oommen, Abdolreza Shirvani, B. Oommen, 2017, J. Comput. Sci..

Cybernetics and Learning Automata

B. John Oommen, Sudip Misra, B. Oommen, 2009, Handbook of Automation.

A GENERALIZED QUERY SYSTEM

B. J. Oommen, R. S. Valiveti, J. R. Zgierski, 1995 .

Enhancing Data Retrieval using Artificially synthesized Queries

B. J. Oommen, D.T.H. Ng, B. Oommen, 1992, Computer Science and Operations Research.

Deterministic Optimal and Expedient Move-to-Rear List Organizing Strategies

B. John Oommen, J. Ian Munro, E. R. Hansen, 1990, Theor. Comput. Sci..

Optimal Constant Space Move-to-Rear List Organization

B. John Oommen, David T. H. Ng, B. Oommen, 1989, Optimal Algorithms.

Expedient Stochastic Move-to-Front and optimal Move-to-Rear List Organizing Strategies

B. John Oommen, E. R. Hansen, B. Oommen, 1986, ICDT.

Occlusion-based estimation of independent multinomial random variables using occurrence and sequential information

B. John Oommen, Sang-Woon Kim, B. Oommen, 2017, Eng. Appl. Artif. Intell..

On the Foundations of Multinomial Sequence Based Estimation

B. John Oommen, Sang-Woon Kim, B. Oommen, 2016, ICCCI.

Optimal sampling for estimation with constrained resources using a learning automaton-based solution for the nonlinear fractional knapsack problem

B. John Oommen, Ole-Christoffer Granmo, B. Oommen, 2010, Applied Intelligence.

On the estimation of independent binomial random variables using occurrence and sequential information

B. John Oommen, Sang-Woon Kim, Geir Horn, 2007, Pattern Recognit..

On the Theory and Applications of Sequence Based Estimation of Independent Binomial Random Variables

B. John Oommen, Sang-Woon Kim, Geir Horn, 2006, SSPR/SPR.

Stochastic learning-based weak estimation of multinomial random variables and its applications to pattern recognition in non-stationary environments

B. John Oommen, Luis Rueda, B. Oommen, 2006, Pattern Recognit..

A New Family of Weak Estimators for Training in Non-stationary Distributions

B. John Oommen, Luis Rueda, B. Oommen, 2004, SSPR/SPR.

On Utilizing Attribute Cardinality Maps to Enhance Query Optimization in the Oracle Database System

B. John Oommen, Jing Chen, B. Oommen, 2006, International Conference on Enterprise Information Systems.

Spikes annihilation in the Hodgkin-Huxley neuron

B. John Oommen, Doron Nussbaum, Dragos Calitoiu, 2008, Biological Cybernetics.

A Kohonen-like decomposition method for the Euclidean traveling salesman problem-KNIES_DECOMPOSE

B. John Oommen, I. K. Altinel, I. Kuban Altinel, 2003, IEEE Trans. Neural Networks.

Large scale modeling of the piriform cortex for analyzing antiepileptic effects

B. John Oommen, Doron Nussbaum, Dragos Calitoiu, 2008, SpringSim '08.

Stochastic Learning-based Weak Estimation and Its Applications

B. John Oommen, Luis Rueda, B. Oommen, 2011 .

An efficient compression scheme for data communication which uses a new family of self‐organizing binary search trees

B. John Oommen, Luis Rueda, B. Oommen, 2008, Int. J. Commun. Syst..

Stochastic Automata-Based Estimators for Adaptively Compressing Files With Nonstationary Distributions

B.J. Oommen, L. Rueda, B. Oommen, 2006, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

A fast and efficient nearly-optimal adaptive Fano coding scheme

B. John Oommen, Luis Rueda, B. Oommen, 2006, Inf. Sci..

On Families of New Adaptive Compression Algorithms Suitable for Time-Varying Source Data

B. John Oommen, Luis Rueda, B. Oommen, 2004, ADVIS.

A nearly-optimal Fano-based coding algorithm

B. John Oommen, Luis Rueda, B. Oommen, 2004, Inf. Process. Manag..

Greedy adaptive Fano coding

B. J. Oommen, B. Oommen, L. Rueda, 2002, Proceedings, IEEE Aerospace Conference.

Enhanced static Fano coding

B. John Oommen, Luis Rueda, B. Oommen, 2001, 2001 IEEE International Conference on Systems, Man and Cybernetics. e-Systems and e-Man for Cybernetics in Cyberspace (Cat.No.01CH37236).

Computing the Vulnerable Phase in a 2D Discrete Model of the Hodgkin-Huxley Neuron

B. John Oommen, Doron Nussbaum, Dragos Calitoiu, 2008 .

On achieving intelligent traffic-aware consolidation of virtual machines in a data center using Learning Automata

Anis Yazidi, B. John Oommen, Kyrre M. Begnum, 2016, J. Comput. Sci..

Achieving Intelligent Traffic-Aware Consolidation of Virtual Machines in a Data Center Using Learning Automata

Anis Yazidi, Kyrre M. Begnum, Kyrre Begnum, 2016, 2016 8th IFIP International Conference on New Technologies, Mobility and Security (NTMS).

Vector Quantization for Arbitrary Distance Function Estimation

B. John Oommen, I. K. Altinel, I. Kuban Altinel, 1997, INFORMS J. Comput..

Modeling a Student's Behavior in a Tutorial-Like System Using Learning Automata

B. John Oommen, M. Khaled Hashem, B. Oommen, 2010, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

Modeling a Student–Classroom Interaction in a Tutorial-Like System Using Learning Automata

B. John Oommen, M. Khaled Hashem, B. Oommen, 2010, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

IEEE TRANSACTIONS ® ON SYSTEMS, MAN, AND CYBERNETICS Editor-in-Chief

P. Borne, Eugene Santos, Vice President, 2009 .

On Using Prototype Reduction Schemes to Optimize Kernel-Based Fisher Discriminant Analysis

B. John Oommen, Sang-Woon Kim, B. Oommen, 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

A formal proof of the ε-optimality of absorbing continuous pursuit algorithms using the theory of regular functions

B. Oommen, Ole-Christoffer Granmo, Lei Jiao, 2014, Applied intelligence (Boston).

Recent advances in Learning Automata systems

B. John Oommen, B. Oommen, 2010, 2010 2nd International Conference on Computer Engineering and Technology.

Dynamic algorithms for the shortest path routing problem: learning automata-based solutions

B. John Oommen, Sudip Misra, B. Oommen, 2005, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

A novel abstraction for swarm intelligence: particle field optimization

B. John Oommen, Nathan Bell, B. Oommen, 2017, Autonomous Agents and Multi-Agent Systems.

Particle Field Optimization: A New Paradigm for Swarm Intelligence

B. John Oommen, Nathan Bell, B. Oommen, 2015, AAMAS.

An Optimal Absorbing List Organization Strategy with Constant Memory Requirements

B. John Oommen, David T. H. Ng, B. Oommen, 1993, Theor. Comput. Sci..

A formal proof of the 𝜖-optimality of discretized pursuit algorithms

B. John Oommen, Xuan Zhang, Ole-Christoffer Granmo, 2016, Applied Intelligence.

Switching models for nonstationary random environments

B. John Oommen, Hassan Masum, B. Oommen, 1995, IEEE Trans. Syst. Man Cybern..

Logistic Neural Networks: Their chaotic and pattern recognition properties

B. John Oommen, Qin Ke, B. Oommen, 2014, Neurocomputing.

Ideal Chaotic Pattern Recognition Is Achievable: The Ideal-M-AdNN - Its Design and Properties

B. John Oommen, Ke Qin, B. Oommen, 2013, Trans. Comput. Collect. Intell..

Networking logistic neurons can yield chaotic and pattern recognition properties

B. John Oommen, Ke Qin, B. Oommen, 2011, 2011 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications (CIMSA) Proceedings.

Chaotic and pattern recognition properties of a network of Logistic neurons

B. John Oommen, Ke Qin, B. Oommen, 2010, 2010 2nd International Conference on Computer Engineering and Technology.

On Utilizing Stochastic Non-linear Fractional Bin Packing to Resolve Distributed Web Crawling

Anis Yazidi, B. John Oommen, Ole-Christoffer Granmo, 2014, 2014 IEEE 17th International Conference on Computational Science and Engineering.

A New Geometric Tool for Pattern Recognition - An Algorithm for Real Time Insertion of Layered Segment Trees

B. John Oommen, Sridhar Radhakrishnan, Gopal Racherla, 2001, ICAPR.

Investigating schizophrenia using local connectivity considerations within the piriform cortex

B.J. Oommen, D. Calitoiu, D. Nussbaum, 2005, Canadian Conference on Electrical and Computer Engineering, 2005..

The Science and Art of Chaotic Pattern Recognition

B. John Oommen, Qin Ke, Calitoiu Dragos, 2016 .

The entire range of Chaotic pattern recognition properties possessed by the Adachi neural network

B. John Oommen, Ke Qin, B. Oommen, 2012, Intell. Decis. Technol..

Desynchronizing a Chaotic Pattern Recognition Neural Network to Model Inaccurate Perception

B. John Oommen, Doron Nussbaum, Dragos Calitoiu, 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

Imposing tree-based topologies onto self organizing maps

B. John Oommen, César A. Astudillo, B. Oommen, 2011, Inf. Sci..

On the classification of dynamical data streams using novel "Anti-Bayesian" techniques

Anis Yazidi, B. John Oommen, Hugo Hammer, 2018, Pattern Recognit..

On using novel “Anti-Bayesian” techniques for the classification of dynamical data streams

Anis Yazidi, B. John Oommen, Hugo Hammer, 2017, 2017 IEEE Congress on Evolutionary Computation (CEC).

Service selection in stochastic environments: a learning-automaton based solution

Anis Yazidi, B. John Oommen, Ole-Christoffer Granmo, 2012, Applied Intelligence.

On Utilizing Association and Interaction Concepts for Enhancing Microaggregation in Secure Statistical Databases

B. John Oommen, Ebaa Fayyoumi, B. Oommen, 2010, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

A Novel Method for Micro-Aggregation in Secure Statistical Databases Using Association and Interaction

B. John Oommen, Ebaa Fayyoumi, B. Oommen, 2007, ICICS.

On utilizing weak estimators to achieve the online classification of data streams

Anis Yazidi, B. John Oommen, Hanane Tavasoli, 2019, Eng. Appl. Artif. Intell..

Multi-Minimax: A New AI Paradigm for Simultaneously-Played Multi-player Games

B. John Oommen, Nicolas Perez, B. Oommen, 2019, Australasian Conference on Artificial Intelligence.

An Adaptive Approach to Learning the Preferences of Users in a Social Network Using Weak Estimators

Anis Yazidi, B. John Oommen, Ole-Christoffer Granmo, 2012, J. Inf. Process. Syst..

A User-Centric Approach for Personalized Service Provisioning in Pervasive Environments

Anis Yazidi, B. John Oommen, Frank Reichert, 2011, Wirel. Pers. Commun..

An intelligent architecture for service provisioning in pervasive environments

B. John Oommen, Anis Yazidi, Frank Reichert, 2011, 2011 International Symposium on Innovations in Intelligent Systems and Applications.

On Using Learning Automata for Fast Graph Partitioning

B. John Oommen, Edward V. de St. Croix, B. Oommen, 1995, LATIN.

Analytic Results on the Hodgkin-Huxley Neural Network: Spikes Annihilation

B. John Oommen, Doron Nussbaum, Dragos Calitoiu, 2007, Canadian Conference on AI.

Epsilon-optimal stubborn learning mechanisms

B. John Oommen, Jens Peter Reus Christensen, J. Christensen, 1990, IEEE Trans. Syst. Man Cybern..

Discretized learning automata solutions to the capacity assignment problem for prioritized networks

B. John Oommen, T. Dale Roberts, B. Oommen, 2002, IEEE Trans. Syst. Man Cybern. Part B.

Continuous Learning Automata Solutions to the Capacity Assignment Problem

B. John Oommen, T. Dale Roberts, B. Oommen, 2000, IEEE Trans. Computers.

On Solving the Capacity Assignment Problem Using Continous Learning Automata

B. John Oommen, T. Dale Roberts, B. Oommen, 1999, IEA/AIE.

A Fast Efficient Solution to the Capacity Assignment Problem Using Discretized Learning Automata

B. John Oommen, T. Dale Roberts, B. Oommen, 1998, IEA/AIE.

Pattern recognition of strings containing traditional and generalized transposition errors

B. J. Oommen, R.K.S. Loke, B. Oommen, 1995, 1995 IEEE International Conference on Systems, Man and Cybernetics. Intelligent Systems for the 21st Century.

Goal-oriented optimal subset selection of correlated multimedia streams

Mohan S. Kankanhalli, Pradeep K. Atrey, B. John Oommen, 2007, TOMCCAP.

On the Cryptanalysis of Two Cryptographic Algorithms That Utilize Chaotic Neural Networks

B. John Oommen, Ke Qin, B. Oommen, 2015 .

On utilizing the transitivity pursuit-enhanced object partitioning to optimize self-organizing lists-on-lists

B. John Oommen, O. Ekaba Bisong, B. Oommen, 2021, Evolving Systems.

Optimizing Self-organizing Lists-on-Lists Using Transitivity and Pursuit-Enhanced Object Partitioning

B. John Oommen, O. Ekaba Bisong, B. Oommen, 2020, AIAI.

On utilizing an enhanced object partitioning scheme to optimize self-organizing lists-on-lists

B. John Oommen, O. Ekaba Bisong, B. Oommen, 2020, Evol. Syst..

On utilizing an enhanced object partitioning scheme to optimize self-organizing lists-on-lists

B. Oommen, O. E. Bisong, 2020, Evolving Systems.

The Hierarchical Continuous Pursuit Learning Automation: A Novel Scheme for Environments With Large Numbers of Actions

Anis Yazidi, Xuan Zhang, Lei Jiao, 2020, IEEE Transactions on Neural Networks and Learning Systems.

The Hierarchical Continuous Pursuit Learning Automation for Large Numbers of Actions

Anis Yazidi, B. John Oommen, Xuan Zhang, 2018, AIAI.

On incorporating the paradigms of discretization and Bayesian estimation to create a new family of pursuit learning automata

B. John Oommen, Xuan Zhang, Ole-Christoffer Granmo, 2013, Applied Intelligence.

Learning-Automaton-Based Online Discovery and Tracking of Spatiotemporal Event Patterns

Anis Yazidi, B. John Oommen, Ole-Christoffer Granmo, 2013, IEEE Transactions on Cybernetics.

Solving Multiconstraint Assignment Problems Using Learning Automata

B. John Oommen, Geir Horn, B. Oommen, 2010, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

Robot navigation in unknown terrains using learned visibility graphs. Part I: The disjoint convex obstacle case

S. Sitharama Iyengar, B. John Oommen, Rangasami L. Kashyap, 1987, IEEE Journal on Robotics and Automation.

On Solving the Problem of Identifying Unreliable Sensors Without a Knowledge of the Ground Truth: The Case of Stochastic Environments

Anis Yazidi, B. John Oommen, Morten Goodwin, 2017, IEEE Transactions on Cybernetics.

On Distinguishing between Reliable and Unreliable Sensors Without a Knowledge of the Ground Truth

Anis Yazidi, B. John Oommen, Morten Goodwin Olsen, 2015, 2015 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT).

Graph Partitioning Using Learning Automata

B. John Oommen, Edward V. de St. Croix, B. Oommen, 1996, IEEE Trans. Computers.

A Novel Strategy for Solving the Stochastic Point Location Problem Using a Hierarchical Searching Scheme

B. John Oommen, Anis Yazidi, Ole-Christoffer Granmo, 2014, IEEE Transactions on Cybernetics.

A Hierarchical Learning Scheme for Solving the Stochastic Point Location Problem

Anis Yazidi, B. John Oommen, Ole-Christoffer Granmo, 2012, IEA/AIE.

Using Artificial Intelligence Techniques for Strategy Generation in the Commons Game

B. John Oommen, Petro Verkhogliad, B. Oommen, 2011, HAIS.

Generalized Bayesian Pursuit: A Novel Scheme for Multi-Armed Bernoulli Bandit Problems

B. John Oommen, Xuan Zhang, Ole-Christoffer Granmo, 2011, EANN/AIAI.

The Bayesian Pursuit Algorithm: A New Family of Estimator Learning Automata

B. John Oommen, Xuan Zhang, Ole-Christoffer Granmo, 2011, IEA/AIE.

On using prototype reduction schemes to optimize kernel-based nonlinear subspace methods

B. John Oommen, Sang-Woon Kim, B. Oommen, 2004, Pattern Recognit..

The object migration automata: its field, scope, applications, and future research challenges

Rebekka Olsson Omslandseter, B. Oommen, L. Jiao, 2023, Pattern Analysis and Applications.

Learning automata-based partitioning algorithms for stochastic grouping problems with non-equal partition sizes

Rebekka Olsson Omslandseter, B. Oommen, Lei Jiao, 2023, Pattern Analysis and Applications.

On enhancing the deadlock-preventing object migration automaton using the pursuit paradigm

B. John Oommen, Abdolreza Shirvani, B. Oommen, 2019, Pattern Analysis and Applications.

On Utilizing the Pursuit Paradigm to Enhance the Deadlock-Preventing Object Migration Automaton

B. John Oommen, Abdolreza Shirvani, B. Oommen, 2017, 2017 International Conference on New Trends in Computing Sciences (ICTCS).

Achieving Microaggregation for Secure Statistical Databases Using Fixed-Structure Partitioning-Based Learning Automata

B. John Oommen, Ebaa Fayyoumi, B. Oommen, 2009, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

A Fixed Structure Learning Automaton Micro-aggregation Technique for Secure Statistical Databases

B. John Oommen, Ebaa Fayyoumi, B. Oommen, 2006, Privacy in Statistical Databases.

Breaking Substitution Cyphers Using Stochastic Automata

B. J. Oommen, J. R. Zgierski, B. Oommen, 1993, IEEE Trans. Pattern Anal. Mach. Intell..

Fast Learning Automaton-Based Image Examination and Retrieval

B. John Oommen, Chris Fothergill, B. Oommen, 1993, Comput. J..

A Conclusive Analysis of the Finite-Time Behavior of the Discretized Pursuit Learning Automaton

Xuan Zhang, Ole-Christoffer Granmo, Lei Jiao, 2020, IEEE Transactions on Neural Networks and Learning Systems.

Discretized Bayesian Pursuit - A New Scheme for Reinforcement Learning

B. John Oommen, Xuan Zhang, Ole-Christoffer Granmo, 2012, IEA/AIE.

Learning Automata-Based Solutions to the Nonlinear Fractional Knapsack Problem With Applications to Optimal Resource Allocation

B. John Oommen, Ole-Christoffer Granmo, Morten Goodwin Olsen, 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

Continuous and discretized pursuit learning schemes: various algorithms and their comparison

B. John Oommen, M. Agache, B. Oommen, 2001, IEEE Trans. Syst. Man Cybern. Part B.

Object Migration Automata for Non-equal Partitioning Problems with Known Partition Sizes

B. John Oommen, Lei Jiao, Rebekka Olsson Omslandseter, 2021, AIAI.

A formal approach to using data distributions for building causal polytree structures

Stan Matwin, B. John Oommen, M. Ouerd, 2004, Inf. Sci..

A Formalism for Building Causal Polytree Structures Using Data Distributions

Stan Matwin, B. John Oommen, M. Ouerd, 2000, ISMIS.

Spelling correction using probabilistic methods

B. John Oommen, Rangasami L. Kashyap, R. Kashyap, 1984, Pattern Recognit. Lett..

Anomaly Detection in Dynamic Systems Using Weak Estimators

B. John Oommen, Justin Zhijun Zhan, Johanna Crisostomo, 2011, TOIT.

Anomaly Detection in Dynamic Social Systems Using Weak Estimators

B. John Oommen, Justin Zhijun Zhan, Johanna Crisostomo, 2009, 2009 International Conference on Computational Science and Engineering.

Multi-class pairwise linear dimensionality reduction using heteroscedastic schemes

B. John Oommen, Luis Rueda, Claudio Henríquez, 2010, Pattern Recognit..

Ε-optimal Discretized Linear Reward-penalty Learning Automata

B.J. Oommen, J.P.R. Christensen, J. Christensen, 1988, IEEE Trans. Syst. Man Cybern..

A survey on statistical disclosure control and micro‐aggregation techniques for secure statistical databases

B. John Oommen, Ebaa Fayyoumi, B. Oommen, 2010, Softw. Pract. Exp..

An Application of a Game of Discrete Generalised Pursuit Automata to Solve a Multi-Constraint Partitioning Problem

B. John Oommen, Geir Horn, B. Oommen, 2006, 2006 IEEE International Conference on Systems, Man and Cybernetics.

A fixed-structure learning automaton solution to the stochastic static mapping problem

B. John Oommen, Geir Horn, B. Oommen, 2005, 19th IEEE International Parallel and Distributed Processing Symposium.

Novel threat-based AI strategies that incorporate adaptive data structures for multi-player board games

B. John Oommen, Spencer Polk, B. Oommen, 2018, Applied Intelligence.

Novel AI Strategies for Multi-Player Games at Intermediate Board States

B. John Oommen, Spencer Polk, B. Oommen, 2015, IEA/AIE.

On Applying Adaptive Data Structures to Multi-Player Game Playing

B. John Oommen, Spencer Polk, B. Oommen, 2013, SGAI Conf..

On Enhancing Recent Multi-player Game Playing Strategies Using a Spectrum of Adaptive Data Structures

B. John Oommen, Spencer Polk, B. Oommen, 2013, 2013 Conference on Technologies and Applications of Artificial Intelligence.

Enhancing Micro-Aggregation Technique by Utilizing Dependence-Based Information in Secure Statistical Databases

B. John Oommen, Ebaa Fayyoumi, B. Oommen, 2008, ACISP.

Routing Bandwidth-Guaranteed Paths in MPLS Traffic Engineering: A Multiple Race Track Learning Approach

Sudip Misra, Ole-Christoffer Granmo, B. John Oommen, 2007, IEEE Transactions on Computers.

Breadth-first search strategies for trie-based syntactic pattern recognition

Ghada Hany Badr, B. John Oommen, B. Oommen, 2007, Pattern Analysis and Applications.

Deterministic Majority filters applied to stochastic sorting

B. John Oommen, Doron Nussbaum, Jack R. Zgierski, 2004, ACM-SE 42.

On Achieving History-Based Move Ordering in Adversarial Board Games Using Adaptive Data Structures

B. John Oommen, Spencer Polk, B. Oommen, 2016, Trans. Comput. Collect. Intell..

A brief taxonomy and ranking of creative prototype reduction schemes

B. John Oommen, Sang-Woon Kim, B. Oommen, 2003, Pattern Analysis & Applications.

Learning automata-based solutions to the optimal web polling problem modelled as a nonlinear fractional knapsack problem

B. John Oommen, Ole-Christoffer Granmo, B. Oommen, 2011, Eng. Appl. Artif. Intell..

Solving Stochastic Nonlinear Resource Allocation Problems Using a Hierarchy of Twofold Resource Allocation Automata

B. John Oommen, Ole-Christoffer Granmo, B. Oommen, 2010, IEEE Transactions on Computers.

Learning Automata-Based Solutions to Stochastic Nonlinear Resource Allocation Problems

B. John Oommen, Ole-Christoffer Granmo, B. Oommen, 2009, Intelligent Systems for Knowledge Management.

A Hierarchy of Twofold Resource Allocation Automata Supporting Optimal Sampling

B. John Oommen, Ole-Christoffer Granmo, B. Oommen, 2009, IEA/AIE.

Determining Optimal Polling Frequency Using a Learning Automata-based Solution to the Fractional Knapsack Problem

B.J. Oommen, B. Oommen, O.-C. Granmo, 2006, 2006 IEEE Conference on Cybernetics and Intelligent Systems.

Keyboard optimization using genetic techniques

B. J. Oommen, J. R. Zgierski, B. Oommen, 1991, [1991 Proceedings] Tenth Annual International Phoenix Conference on Computers and Communications.

PATTERN RECOGNITION WITH STRINGS, SUBSTRINGS AND BOUNDARIES

Basantkumar John Oommen, B. Oommen, 1982 .

A formal theory for optimal and information theoretic syntactic pattern recognition

B. John Oommen, Rangasami L. Kashyap, R. Kashyap, 1998, Pattern Recognit..

The Normalized String Editing Problem Revisited

B. John Oommen, K. Zhang, B. Oommen, 1996, IEEE Trans. Pattern Anal. Mach. Intell..

Noisy Subsequence Recognition Using Constrained String Editing Involving Substitutions, Insertions, Deletions and Generalized Transpositions

B. John Oommen, Richard K. S. Loke, B. Oommen, 1995, ICSC.

String Alignment with Substitution, Insertion, Deletion, Squashing and Expansion Operations

B. Oommen, 1995, Inf. Sci..

Recognizing Sources of Random Strings

B. John Oommen, Radhakrishna S. Valiveti, B. Oommen, 1991, IEEE Trans. Pattern Anal. Mach. Intell..

Algorithms for String Editing which Permit Arbitrarily Complex Editing Constraints

B. John Oommen, B. Oommen, 1984, MFCS.

SYMBOLIC CHANNEL MODELLING FOR NOISY CHANNELS WHICH PERMIT ARBITRARY NOISE DISTRIBUTIONS

R. L. Kashyap, B. J. Oommen, R. Kashyap, 2000 .

NOISY SUBSEQUENCE RECOGNITION USING CONSTRAINED STRING EDITING INVOLVING ARBITRARY OPERATIONS*

B. Oommen, R. Loke, 1994 .

Recognition ofNoisySubsequencesUsing Constrained EditDistances

B. John Oommen, B. Oommen, 1987 .

Similarity measures for sets of strings

R. L. Kashyap, B. J. Oommen, R. Kashyap, 1983 .

Cryptanalysis of a Cryptographic Algorithm that Utilizes Chaotic Neural Networks

B. John Oommen, Ke Qin, B. Oommen, 2014, ISCIS.

Adaptive learning mechanisms for ordering actions using random races

B. John Oommen, David T. H. Ng, E. R. Hansen, 1993, IEEE Trans. Syst. Man Cybern..

Recent Results on Learning from Stochastic Teachers and Compulsive Liars/Con-Men

B. John Oommen, B. Oommen, 2004, Pattern Recognition in Information Systems.

On generating random permutations with arbitrary distributions

B. John Oommen, David T. H. Ng, B. Oommen, 1989, International Conference on Scientific Computing.

A novel technique for stochastic root-finding: Enhancing the search with adaptive d-ary search

Anis Yazidi, B. John Oommen, B. Oommen, 2017, Inf. Sci..

Optimal and Information Theoretic Syntactic Pattern Recognition Involving Traditional and Transposition Errors

B. John Oommen, Richard K. S. Loke, B. Oommen, 1996, FSTTCS.

Channel selection in Cognitive Radio Networks: A Switchable Bayesian Learning Automata approach

B. John Oommen, Xuan Zhang, Ole-Christoffer Granmo, 2013, 2013 IEEE 24th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC).

"Anti-Bayesian" flat and hierarchical clustering using symmetric quantiloids

Anis Yazidi, B. John Oommen, Hugo Hammer, 2016, Inf. Sci..

A Novel Clustering Algorithm Based on a Non-parametric "Anti-Bayesian" Paradigm

Anis Yazidi, B. John Oommen, Hugo Hammer, 2015, IEA/AIE.