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Vitenskapelige publikasjoner

  • Zhang, Xuan; Jiao, Lei; Oommen, John; Granmo, Ole-Christoffer (2019). A Conclusive Analysis of the Finite-Time Behavior of the Discretized Pursuit Learning Automaton. IEEE Transactions on Neural Networks and Learning Systems. ISSN: 2162-237X. doi:10.1109/TNNLS.2019.2900639.
  • Shirvani, Abdolreza; Oommen, John (2019). On enhancing the deadlock-preventing object migration automaton using the pursuit paradigm. Pattern Analysis and Applications. ISSN: 1433-7541. doi:10.1007/s10044-019-00817-z.
  • Shirvani, Abdolreza; Oommen, John (2019). The Power of the “Pursuit” Learning Paradigm in the Partitioning of Data. IFIP Advances in Information and Communication Technology. ISSN: 1868-4238. 559s 3 - 16. doi:10.1007/978-3-030-19823-7_1.
  • Shirvani, Abdolreza; Oommen, John (2019). The power of the “pursuit” learning paradigm in the partitioning of data. Communications in Computer and Information Science. ISSN: 1865-0929. 1000s 3 - 16. doi:10.1007/978-3-030-20257-6_1.
  • McMahon, Thomas; Oommen, John (2018). Enhancing English-Japanese Translation Using Syntactic Pattern Recognition Methods. Proceedings of the 10th International Conference on Computer Recognition Systems CORES 2017. ISBN: 9783319591612. Springer. chapter.
  • Yazidi, Anis; Hammer, Hugo Lewi; Oommen, John (2018). Higher-Fidelity Frugal and Accurate Quantile Estimation Using a Novel Incremental Discretized Paradigm. IEEE Access. ISSN: 2169-3536. 6s 24362 - 24374. doi:10.1109/ACCESS.2018.2820501.
  • Havelock, Jessica; Oommen, John; Granmo, Ole-Christoffer (2018). Novel Distance Estimation Methods Using 'Stochastic Learning on the Line' Strategies. IEEE Access. ISSN: 2169-3536. 6s 48438 - 48454. doi:10.1109/ACCESS.2018.2868233.
  • Yazidi, Anis; Oommen, John (2018). Novel Results on Random Walk-Jump Chains That Possess Tree-Based Transitions. Advances in Intelligent Systems and Computing. ISSN: 2194-5357. 578s 43 - 52. doi:10.1007/978-3-319-59162-9_5.
  • Taucer, Armando H.; Polk, Spencer; Oommen, John (2018). On Addressing the Challenges of Complex Stochastic Games Using “Representative” Moves. Artificial Intelligence Applications and Innovations. ISBN: 978-3-319-92006-1. Springer. chapter. s 3 - 13.
  • Shirvani, Abdolreza; Oommen, John (2018). On Invoking Transitivity to Enhance the Pursuit-Oriented Object Migration Automata. IEEE Access. ISSN: 2169-3536. 6s 21668 - 21681. doi:10.1109/ACCESS.2018.2827305.
  • Jobava, Akaki; Yazidi, Anis; Oommen, John; Begnum, Kyrre (2017). On achieving intelligent traffic-aware consolidation of virtual machines in a data center using Learning Automata. Journal of Computational Science. ISSN: 1877-7503. 24s 290 - 312. doi:10.1016/j.jocs.2017.08.005.
  • Mohan, Ratish; Yazidi, Anis; Feng, Boning; Oommen, John (2018). On optimizing firewall performance in dynamic networks by invoking a novel swapping window-based paradigm. International Journal of Communication Systems. ISSN: 1074-5351. 31 (15). doi:10.1002/dac.3773.
  • Hammer, Hugo Lewi; Yazidi, Anis; Oommen, John (2018). On the Classification of Dynamical Data Streams Using Novel “Anti–Bayesian” Techniques. Pattern Recognition. ISSN: 0031-3203. 76s 108 - 124. doi:10.1016/j.patcog.2017.10.031.
  • Yazidi, Anis; Oommen, John (2018). On the analysis of a random walk-jump chain with tree-based transitions and its applications to faulty dichotomous search. Sequential Analysis. ISSN: 0747-4946. 37 (1). s 31 - 46. doi:10.1080/07474946.2018.1427971.
  • Havelock, Jessica; Oommen, John; Granmo, Ole-Christoffer (2018). On using "Stochastic learning on the line" to design novel distance estimation methods. Lecture Notes in Computer Science. ISSN: 0302-9743. 10868 LNAIs 34 - 42. doi:10.1007/978-3-319-92058-0_4.
  • Yazidi, Anis; Zhang, Xuan; Lei, Jiao; Oommen, John (2018). The Hierarchical Continuous Pursuit Learning Automation for Large Numbers of Actions. Artificial Intelligence Applications and Innovations. ISBN: 978-3-319-92006-1. Springer. Chapter. s 451 - 461.
  • Yazidi, Anis; Hammer, Hugo Lewi; Oommen, John (2017). A higher-fidelity frugal quantile estimator. Lecture Notes in Computer Science. ISSN: 0302-9743. 10604 LNAIs 76 - 86. doi:10.1007/978-3-319-69179-4_6.
  • Thapa, Rajan; Lei, Jiao; Oommen, John; Yazidi, Anis (2017). A learning automaton-based scheme for scheduling domestic shiftable loads in smart grids. IEEE Access. ISSN: 2169-3536. 6s 5348 - 5361. doi:10.1109/ACCESS.2017.2788051.
  • Yazidi, Anis; Oommen, John (2017). A novel technique for stochastic root-finding: Enhancing the search with adaptive d-ary search. Information Sciences. ISSN: 0020-0255. 393s 108 - 129. doi:10.1016/j.ins.2017.02.014.
  • Polk, Spencer; Oommen, John (2017). Challenging state-of-the-art move ordering with Adaptive Data Structures. Applied intelligence (Boston). ISSN: 0924-669X. s 1 - 20. doi:10.1007/s10489-017-1006-0.
  • Yazidi, Anis; Oommen, John; Goodwin, Morten (2017). Identifying Unreliable Sensors Without a Knowledge of the Ground Truth in Deceptive Environments. Lecture Notes in Computer Science. ISSN: 0302-9743. 10604s 741 - 753. doi:10.1007/978-3-319-69179-4_52.
  • Oommen, John; Kim, Sang-Woon (2017). Occlusion-based estimation of independent multinomial random variables using occurrence and sequential information. Engineering applications of artificial intelligence. ISSN: 0952-1976. 63s 69 - 84. doi:10.1016/j.engappai.2017.05.001.
  • Hammer, Hugo Lewi; Yazidi, Anis; Oommen, John (2017). On Using Novel "Anti-Bayesian" Techniques for the Classification of Dynamical Data Streams. 2017 IEEE Congress on Evolutionary Computation (CEC). ISBN: 978-1-5090-4601-0. IEEE conference proceedings. Proceedings. s 1173 - 1182.
  • Shirvani, Abdolreza; Oommen, John (2017). On Utilizing the Pursuit Paradigm to Enhance the Deadlock-Preventing Object Migration Automaton. International Conference on New Trends in Computing Sciences, ICTCS 2017. ISBN: 978-1-5386-0527-1. IEEE conference proceedings. chapter. s 295 - 302.
  • Shirvani, Abdolreza; Oommen, John (2017). On enhancing the object migration automaton using the Pursuit paradigm. Journal of Computational Science. ISSN: 1877-7503. 24s 329 - 342. doi:10.1016/j.jocs.2017.08.008.
  • Shirvani, Abdolreza; Oommen, John (2017). Partitioning in signal processing using the object migration automaton and the pursuit paradigm. 2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP). ISBN: 978-1-5090-6341-3. IEEE Signal Processing Society. chapter.
  • Thapa, Rajan; Lei, Jiao; Oommen, John; Yazidi, Anis (2017). Scheduling domestic shiftable loads in smart grids: A learning automata-based scheme. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. ISSN: 1867-8211. 203s 58 - 68. doi:10.1007/978-3-319-61813-5_6.
  • Hammer, Hugo Lewi; Yazidi, Anis; Oommen, John (2017). “Anti-Bayesian” flat and hierarchical clustering using symmetric quantiloids. Information Sciences. ISSN: 0020-0255. 418-419s 495 - 512. doi:10.1016/j.ins.2017.08.017.
  • Astudillo, César A.; Poblete, Jorge; Resta, Marina; Oommen, John (2016). A Cluster Analysis of Stock Market Data Using Hierarchical SOMs. AI 2016: Advances in Artificial Intelligence. ISBN: 978-3-319-50126-0. Springer. chapter. s 101 - 112.
  • Bell, Nathan; Oommen, John (2016). A novel abstraction for swarm intelligence: particle field optimization. Autonomous Agents and Multi-Agent Systems. ISSN: 1387-2532. 31 (2). s 362 - 385. doi:10.1007/s10458-016-9350-8.
  • Jobava, Akaki; Yazidi, Anis; Oommen, John; Begnum, Kyrre (2016). Achieving Intelligent Traffic-aware Consolidation of Virtual Machines in a Data Center Using Learning Automata. 2016 8th IFIP International Conference on New Technologies, Mobility and Security (NTMS). ISBN: 978-1-5090-2914-3. IEEE conference proceedings. chapter.
  • Polk, Spencer; Oommen, John (2016). Challenging Established Move Ordering Strategies with Adaptive Data Structures. Trends in Applied Knowledge-Based Systems and Data Science. 29th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2016, Morioka, Japan, August 2-4, 2016, Proceedings. ISBN: 978-3-319-42006-6. Springer. chapter. s 862 - 872.
  • Astudillo, César A.; Gonzalez, Javier I.; Oommen, John; Yazidi, Anis (2016). Concept Drift Detection Using Online Histogram-Based Bayesian Classifiers. AI 2016: Advances in Artificial Intelligence. ISBN: 978-3-319-50126-0. Springer. chapter. s 175 - 182.
  • Mohan, Ratish; Yazidi, Anis; Feng, Boning; Oommen, John (2016). Dynamic Ordering of Firewall Rules Using a Novel Swapping Window-based Paradigm. Proceedings of the 6th International Conference on Communication and Network Security (ICCNS '16). ISBN: 978-1-4503-4783-9. Association for Computing Machinery (ACM). 1. s 11 - 20.
  • Oommen, John; Kim, Sang-Woon (2016). Multinomial Sequence Based Estimation Using Contiguous Subsequences of Length Three. 13th International Conference, ICIAR 2016, in Memory of Mohamed Kamel. ISBN: 978-3-319-41501-7. Springer. chapter. s 243 - 253.
  • Yazidi, Anis; Oommen, John (2016). Novel Discretized Weak Estimators Based on the Principles of the Stochastic Search on the Line Problem. IEEE Transactions on Cybernetics. ISSN: 2168-2267. 46 (12). s 2732 - 2744. doi:10.1109/TCYB.2015.2487338.
  • Polk, Spencer; Oommen, John (2016). Novel threat-based AI strategies that incorporate adaptive data structures for multi-player board games. Applied intelligence (Boston). ISSN: 0924-669X. s 1 - 19. doi:10.1007/s10489-016-0835-6.
  • Polk, Spencer; Oommen, John (2016). On Achieving History-Based Move Ordering in Adversarial Board Games using Adaptive Data Structures. Transactions on Computational Collective Intelligence. ISSN: 2190-9288. 9655s 10 - 44. doi:10.1007/978-3-662-49619-0_2.
  • Yazidi, Anis; Oommen, John; Goodwin, Morten (2016). On solving the problem of identifying unreliable sensors without a knowledge of the ground truth: the case of stochastic environments. IEEE Transactions on Cybernetics. ISSN: 2168-2267. 47 (7). s 1604 - 1617. doi:10.1109/TCYB.2016.2552979.
  • Oommen, John; Kim, Sang-Woon (2016). On the Foundations of Multinomial Sequence Based Estimation. Computational Collective Intelligence, 8th International Conference, ICCCI 2016, Halkidiki, Greece, September 28-30, 2016. Proceedings, Part I. ISBN: 978-3-319-45242-5. Springer. chapter. s 218 - 229.
  • Tavasoli, Hanane; Oommen, John; Yazidi, Anis (2016). On the Online Classification of Data Streams Using Weak Estimators. Trends in Applied Knowledge-Based Systems and Data Science. 29th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2016, Morioka, Japan, August 2-4, 2016, Proceedings. ISBN: 978-3-319-42006-6. Springer. chapter. s 68 - 79.
  • Lei, Jiao; Zhang, Xuan; Oommen, John; Granmo, Ole-Christoffer (2016). Optimizing channel selection for cognitive radio networks using a distributed Bayesian learning automata-based approach. Applied intelligence (Boston). ISSN: 0924-669X. 44 (2). s 307 - 321. doi:10.1007/s10489-015-0682-x.
  • Yazidi, Anis; Oommen, John; Horn, Geir Henrik; Granmo, Ole-Christoffer (2016). Stochastic discretized learning-based weak estimation: a novel estimation method for non-stationary environments. Pattern Recognition. ISSN: 0031-3203. 60s 430 - 443. doi:10.1016/j.patcog.2016.05.001.
  • Oommen, John; Khoury, Richard; Schmidt, Aron (2016). Text Classification Using “Anti”-Bayesian Quantile Statistics-Based Classifiers. Transactions on Computational Collective Intelligence. ISSN: 2190-9288. doi:10.1007/978-3-662-53580-6_7.
  • Oommen, John; Qin, Ke; Calitoiu, Dragos (2016). The Science and Art of Chaotic Pattern Recognition. Applications of Chaos Theory. ISBN: 9781466590441. CRC Press. chapter. s 745 - 802.
  • Zhang, Xuan; Oommen, John; Granmo, Ole-Christoffer (2016). The design of absorbing Bayesian pursuit algorithms and the formal analyses of their ε-optimality. Pattern Analysis and Applications. ISSN: 1433-7541. s 1 - 12. doi:10.1007/s10044-016-0535-1.
  • Yazidi, Anis; Hammer, Hugo Lewi; Oommen, John (2016). “Anti-Bayesian” Flat and Hierarchical Clustering Using Symmetric Quantiloids. Trends in Applied Knowledge-Based Systems and Data Science. 29th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2016, Morioka, Japan, August 2-4, 2016, Proceedings. ISBN: 978-3-319-42006-6. Springer. Intelligent Systems in Modeling Phase of Information Mining Development Process. s 56 - 67.
  • Hammer, Hugo Lewi; Yazidi, Anis; Oommen, John (2015). A Novel Clustering Algorithm based on a Non-parametric "Anti-Bayesian" Paradigm. 28th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems. ISBN: 978-3-319-19066-2. Springer. Unsupervised Learning. s 536 - 545.
  • Zhang, Xuan; Oommen, John; Granmo, Ole-Christoffer; Lei, Jiao (2015). A formal proof of the e-optimality of discretized pursuit algorithms. Applied intelligence (Boston). ISSN: 0924-669X. doi:10.1007/s10489-015-0670-1.
  • Polk, Spencer; Oommen, John (2015). Enhancing History-Based Move Ordering in Game Playing Using Adaptive Data Structures. Computational Collective Intelligence, 7th International Conference, ICCCI 2015, Madrid, Spain, September 21-23, 2015, Proceedings, Part I. ISBN: 978-3-319-24069-5. Springer. chapter. s 225 - 235.
  • Polk, Spencer; Oommen, John (2015). Novel AI Strategies for Multi-Player Games at Intermediate Board States. Current Approaches in Applied Artificial Intelligence, 28th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2015, Seoul, South Korea, June 10-12, 2015, Proceedings. ISBN: 978-3-319-19066-2. Springer. Kapittel. s 33 - 42.
  • Yazidi, Anis; Oommen, John; Goodwin, Morten (2015). On Distinguishing between Reliable and Unreliable Sensors Without a Knowledge of the Ground Truth. IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2015, Volume II.. ISBN: 978-1-4673-9618-9. IEEE. Volume II. s 104 - 111.
  • Qin, Ke; Oommen, John (2015). On the Cryptanalysis of Two Cryptographic Algorithms That Utilize Chaotic Neural Networks. Mathematical problems in engineering (Print). ISSN: 1024-123X. 2015doi:10.1155/2015/468567.
  • Bell, Nathan; Oommen, John (2015). Particle Field Optimization: A New Paradigm for Swarm Intelligence. AAMAS '15 Proceedings of the 2015 International Conference on Autonomous Agents and Multiagent Systems. ISBN: 978-1-4503-3413-6. The International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS). Kapittel. s 257 - 265.
  • Astudillo, César A.; Oommen, John (2015). Pattern Recognition using the TTOCONROT. Current Approaches in Applied Artificial Intelligence, 28th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2015, Seoul, South Korea, June 10-12, 2015, Proceedings. ISBN: 978-3-319-19066-2. Springer. kapittel. s 435 - 444.
  • Li, Yifeng; Oommen, John; Ngom, Alioune; Rueda, Luis (2015). Pattern classification using a new border identification paradigm: The nearest border technique. Neurocomputing. ISSN: 0925-2312. 157s 105 - 117. doi:10.1016/j.neucom.2015.01.030.
  • Yazidi, Anis; Oommen, John (2015). Solving Stochastic Root-Finding with adaptive d-ary search. 2015 IEEE International Conference on Evolving and Adaptive Intelligent Systems (EAIS). ISBN: 978-1-4673-6698-4. IEEE. chapter.
  • Polk, Spencer; Oommen, John (2015). Space and depth-related enhancements of the history-ADS strategy in game playing. 2015 IEEE Conference on Computational Intelligence and Games. ISBN: 978-1-4799-8621-7. IEEE conference proceedings. kapittel. s 322 - 327.
  • Oommen, John; Khoury, Richard; Schmidt, Aron (2015). Text Classification Using Novel “Anti-Bayesian” Techniques. Computational Collective Intelligence, 7th International Conference, ICCCI 2015, Madrid, Spain, September 21-23, 2015, Proceedings, Part I. ISBN: 978-3-319-24069-5. Springer. Chapter.
  • Oommen, John; Thomas, A. (2014). "Anti-Bayesian" parametric pattern classification using order statistics criteria for some members of the exponential family. Pattern Recognition. ISSN: 0031-3203. 47 (1). s 40 - 55. doi:10.1016/j.patcog.2013.02.006.
  • Lei, Jiao; Zhang, Xuan; Granmo, Ole-Christoffer; Oommen, John (2014). A Bayesian Learning Automata-Based Distributed Channel Selection Scheme. Modern Advances in Applied Intelligence, 27th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2014,Kaohsiung, Taiwan, June 3-6, 2014, Part II. ISBN: 978-3-319-07455-9. Springer. kapittel. s 48 - 57.
  • Zhang, Xuan; Granmo, Ole-Christoffer; Oommen, John; Lei, Jiao (2014). A formal proof of the ε-optimality of absorbing continuous pursuit algorithms using the theory of regular functions. Applied intelligence (Boston). ISSN: 0924-669X. 41 (3). s 974 - 985. doi:10.1007/s10489-014-0541-1.
  • Yazidi, Anis; Granmo, Ole-Christoffer; Oommen, John; Goodwin, Morten (2014). A novel strategy for solving the stochastic point location problem using a hierarchical searching scheme. IEEE Transactions on Cybernetics. ISSN: 2168-2267. 44 (11). s 2202 - 2220. doi:10.1109/TCYB.2014.2303712.
  • Qin, Ke; Oommen, John (2014). Chaotic Neural Networks with a “Small-World” Topology Can Achieve Pattern Recognition. Chaotic Modeling and Simulation (CMSIM). ISSN: 2241-0503. 4s 379 - 386.
  • Qin, Ke; Oommen, John (2014). Cryptanalysis of a Cryptographic Algorithm that Utilizes Chaotic Neural Networks. Information Science and Systems - Proceedings of the 29th International Symposium on Computer and Information Science. ISBN: 978-3-319-09465-6. Springer. chapter. s 167 - 174.
  • Astudillo, César A.; Oommen, John (2014). Fast BMU Search in SOMs Using Random Hyperplane Trees. PRICAI 2014: Trends in Artificial Intelligence: 13th Pacific Rim International Conference on Artificial Intelligence. ISBN: 9783319135601. Springer. chapter. s 39 - 51.
  • Qin, Ke; Oommen, John (2014). Logistic Neural Networks: Their chaotic and pattern recognition properties. Neurocomputing. ISSN: 0925-2312. 125s 184 - 194. doi:10.1016/j.neucom.2012.10.039.
  • Yazidi, Anis; Oommen, John; Granmo, Ole-Christoffer; Goodwin, Morten (2014). On Utilizing Stochastic Non-linear Fractional Bin Packing to Resolve Distributed Web Crawling. 17th IEEE International Conference on Computational Science and Engineering, CSE 2014. ISBN: 978-1-4799-7981-3. IEEE conference proceedings. kapittel. s 32 - 37.
  • Sakhravi, Rokhsareh; Omran, Masoud T.; Oommen, John (2014). On the Existence and Heuristic Computation of the Solution for the Commons Game. Transactions on Computational Collective Intelligence XIV. ISBN: 9783662445099. Springer. chapter. s 71 - 99.
  • Sakhravi, Rokhsareh; Omran, Masoud T.; Oommen, John (2014). On the existence and heuristic computation of the solution for the commons game. Lecture Notes in Computer Science. ISSN: 0302-9743. 8615s 71 - 99. doi:10.1007/978-3-662-44509-9_4.
  • Astudillo, César A.; Oommen, John (2014). Self Organizing Maps Whose Topologies Can Be Learned With Adaptive Binary Search Trees Using Conditional Rotations. Pattern Recognition. ISSN: 0031-3203. 47 (1). s 96 - 113. doi:10.1016/j.patcog.2013.04.012.
  • Astudillo, César A.; Oommen, John (2014). Topology-oriented self-organizing maps: a survey. Pattern Analysis and Applications. ISSN: 1433-7541. 17 (2). s 223 - 248. doi:10.1007/s10044-014-0367-9.
  • Zhang, Xuan; Oommen, John; Granmo, Ole-Christoffer; Lei, Jiao (2014). Using the Theory of Regular Functions to Formally Prove the ε -Optimality of Discretized Pursuit Learning Algorithms. Modern Advances in Applied Intelligence - 27th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2014, Kaohsiung, Taiwan, June 3-6, 2014, Proceedings, Part I. ISBN: 978-3-319-07467-2. Springer. kapittel. s 379 - 388.
  • Thomas, A.; Oommen, John (2013). A Novel Border Identification Algorithm Based on an “Anti-Bayesian” Paradigm. Computer Analysis of Images and Patterns. ISBN: 978-3-642-40245-6. Springer. kapittel. s 196 - 203.
  • Li, Yifeng; Oommen, John; Ngom, Alioune; Rueda, Luis (2013). A new paradigm for pattern classification: nearest border techniques. AI 2013: Advances in Artificial Intelligence. ISBN: 978-3-319-03679-3. Springer. kapittel. s 441 - 446.
  • Zhang, Xuan; Lei, Jiao; Granmo, Ole-Christoffer; Oommen, John (2013). Channel selection in cognitive radio networks: A switchable Bayesian learning automata approach. 2013 IEEE 24th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC). ISBN: 978-1-4673-6234-4. IEEE conference proceedings. kapittel. s 2362 - 2367.
  • Thomas, A.; Oommen, John (2013). Classification of multi-dimensional distributions using order statistics criteria. Proceedings of the 8th International Conference on Computer Recognition Systems CORES 2013. ISBN: 978-3319009681. Springer Publishing Company. kapittel. s 19 - 29.
  • Qin, Ke; Oommen, John (2013). IdealChaotic Pattern Recognition Is Achievable: The Ideal-M-AdNN - Its Design and Properties. Transactions on Computational Collective Intelligence XI. ISBN: 978-3-642-41775-7. Springer Publishing Company. Kapittel. s 22 - 51.
  • Yazidi, Anis; Granmo, Ole-Christoffer; Oommen, John (2013). Learning-Automaton-Based Online Discovery and Tracking of Spatiotemporal Event Patterns. IEEE Transactions on Cybernetics. ISSN: 2168-2267. 43 (3). s 1118 - 1130. doi:10.1109/TSMCB.2012.2224339.
  • Oommen, John; Hashem, K. (2013). Modeling the "Learning Process" of the Teacher in a Tutorial-Like System Using Learning Automata. IEEE Transactions on Cybernetics. ISSN: 2168-2267. 43 (6). s 2020 - 2031. doi:10.1109/TSMCB.2013.2238230.
  • Thomas, A.; Oommen, John (2013). On Achieving Near-optimal “Anti-Bayesian” Order Statistics-based Classification for Asymmetric Exponential Distributions. Computer Analysis of Images and Patterns. ISBN: 978-3-642-40245-6. Springer. kapittel. s 368 - 376.
  • Astudillo, C.; Oommen, John (2013). On Achieving Semi-Supervised Pattern Recognition by Utilizing Tree-Based SOMs. Pattern Recognition. ISSN: 0031-3203. 46s 293 - 304. doi:10.1016/j.patcog.2012.07.006.
  • Polk, Spencer; Oommen, John (2013). On Enhancing Recent Multi-Player Game Playing Strategies using a Spectrum of Adaptive Data Structures. Proceedings of the 2013 Conference on Technologies and Applications of Artificial Intelligence. ISBN: 978-1-4799-2528-5. IEEE. chapter. s 164 - 169.
  • Zhang, Xuan; Granmo, Ole-Christoffer; Oommen, John; Lei, Jiao (2013). On Using the Theory of Regular Functions to Prove the Epsilon-Optimality of the Continuous Pursuit Learn- ing Automaton. Recent Trends in Applied Artificial Intelligence. ISBN: 978-3-642-38576-6. Springer. kapittel. s 262 - 271.
  • Calitoiu, Dragos; Oommen, John (2013). On Utilizing Nonlinear Interdependence Measures for Analyzing Chaotic Behavior in Large-Scale Neuro-Models. Chaotic Modeling and Simulation (CMSIM). ISSN: 2241-0503. 3s 423 - 430.
  • Polk, Spencer; Oommen, John (2013). On applying adaptive data structures to multi-player game playing. Research and Development in Intelligent Systems XXX. ISBN: 978-3-319-02620-6. Springer Publishing Company. kapittel. s 125 - 138.
  • Zhang, Xuan; Granmo, Ole-Christoffer; Oommen, John (2013). On incorporating the paradigms of discretization and Bayesian estimation to create a new family of pursuit learning automata. Applied intelligence (Boston). ISSN: 0924-669X. 39 (4). s 782 - 792. doi:10.1007/s10489-013-0424-x.
  • Thomas, A.; Oommen, John (2013). Order statistics-based parametric classification for multi-dimensional distributions. Pattern Recognition. ISSN: 0031-3203. 46 (12). s 3472 - 3482. doi:10.1016/j.patcog.2013.04.019.
  • Thomas, A.; Oommen, John (2013). The fundamental theory of optimal "Anti-Bayesian" parametric pattern classification using order statistics criteria. Pattern Recognition. ISSN: 0031-3203. 46 (1). s 376 - 388. doi:10.1016/j.patcog.2012.07.004.
  • Stensby, Aleksander; Granmo, Ole-Christoffer; Oommen, John (2013). The use of weak estimators to achieve language detection and tracking in multilingual documents. International journal of pattern recognition and artificial intelligence. ISSN: 0218-0014. 27 (4). doi:10.1142/S0218001413500110.
  • Thomas, A.; Oommen, John (2013). Ultimate order statistics-based prototype reduction schemes. AI 2013: Advances in Artificial Intelligence. ISBN: 978-3-319-03679-3. Springer. kapittel. s 421 - 433.
  • Sakhravi, Rokhsareh; Omran, Masoud T.; Oommen, John (2012). A Fast Heuristic Solution for the Commons Game. Distributed Computing and Artificial Intelligence : 9th International Conference. ISBN: 978-3-642-28764-0. Springer. Kapittel. s 81 - 90.
  • Yazidi, Anis; Granmo, Ole-Christoffer; Oommen, John; Goodwin, Morten (2012). A Hierarchical Learning Scheme for Solving the Stochastic Point Location Problem. Advanced Research in Applied Artificial Intelligence : 25th International Conference 25th International Conference on Industrial Engineering and OtherApplications of Applied Intelligent Systems, IEA/AIE 2012 Dalian, China, June 9-12, 2012 Proceedings. ISBN: 978-3-642-31086-7. Springer. Kapittel. s 774 - 783.
  • Yazidi, Anis; Oommen, John; Granmo, Ole-Christoffer (2012). A Novel Stochastic Discretized Weak Estimator Operating in Non-Stationary Environments. 2012 International Conference on Computing, Networking and Communications (ICNC). ISBN: 978-1-4673-0009-4. IEEE Communications Society. Kapittel. s 364 - 370.
  • Yazidi, Anis; Granmo, Ole-Christoffer; Oommen, John (2012). A Stochastic Search on the Line-Based Solution to Discretized Estimation. Advanced Research in Applied Artificial Intelligence : 25th International Conference 25th International Conference on Industrial Engineering and OtherApplications of Applied Intelligent Systems, IEA/AIE 2012 Dalian, China, June 9-12, 2012 Proceedings. ISBN: 978-3-642-31086-7. Springer. Kapittel. s 764 - 773.
  • Zhang, Xuan; Granmo, Ole-Christoffer; Oommen, John (2012). Discretized Bayesian Pursuit – A New Scheme for Reinforcement Learning. Advanced Research in Applied Artificial Intelligence : 25th International Conference 25th International Conference on Industrial Engineering and OtherApplications of Applied Intelligent Systems, IEA/AIE 2012 Dalian, China, June 9-12, 2012 Proceedings. ISBN: 978-3-642-31086-7. Springer. Kapittel. s 784 - 793.
  • Oommen, John; Bellinger, C. (2012). Emerging Trends in Machine Learning: Classification of Stochastically Episodic Events. Emerging Paradigms in Machine Learning. ISBN: 978-3-642-28698-8. Springer. Kapittel 7. s 161 - 195.
  • Bellinger, C.; Oommen, John (2012). On the Pattern Recognition and Classification of Stochastically Episodic Events. Transactions on Computational Collective Intelligence VI. ISBN: 978-3-642-29355-9. Springer Publishing Company. kapittel. s 1 - 35.
  • Thomas, A; Oommen, John (2012). Optimal “Anti-Bayesian” Parametric Pattern Classification Using Order Statistics Criteria. Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications : 17th Iberoamerican Congress, CIARP 2012, Buenos Aires, Argentina, September 3-6, 2012. Proceedings. ISBN: 978-3-642-33274-6. Springer. Kapittel. s 1 - 13.
  • Thomas, A; Oommen, John (2012). Optimal “Anti-Bayesian” Parametric Pattern Classification for the Exponential Family Using Order Statistics Criteria. Image Analysis and Recognition 9th International Conference, ICIAR 2012, Aveiro, Portugal, June 25-27, 2012. Proceedings, Part I. ISBN: 978-3-642-31294-6. Springer. Kapittel. s 11 - 18.
  • Oommen, John (2010). Computer Engineering and Technology (ICCET), 2010 2nd International Conference on. ISBN: 978-1-4244-6347-3. IEEE conference proceedings. s 783.

Sist endret: 21.06.2019 14:06

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