| 2009 |
|---|
| 169 | EE | Sudip Misra,
B. John Oommen:
An efficient pursuit automata approach for estimating stable all-pairs shortest paths in stochastic network environments.
Int. J. Communication Systems 22(4): 441-468 (2009) |
| 2008 |
|---|
| 168 | EE | B. John Oommen,
Ebaa Fayyoumi:
Enhancing Micro-Aggregation Technique by Utilizing Dependence-Based Information in Secure Statistical Databases.
ACISP 2008: 404-418 |
| 167 | EE | B. John Oommen,
Ebaa Fayyoumi:
An AI-Based Causal Strategy for Securing Statistical Databases Using Micro-aggregation.
Australasian Conference on Artificial Intelligence 2008: 423-434 |
| 166 | EE | Luis Rueda,
Claudio Henríquez,
B. John Oommen:
Chernoff-Based Multi-class Pairwise Linear Dimensionality Reduction.
CIARP 2008: 301-308 |
| 165 | EE | Sang-Woon Kim,
B. John Oommen:
A Fast Computation of Inter-class Overlap Measures Using Prototype Reduction Schemes.
Canadian Conference on AI 2008: 173-184 |
| 164 | EE | Ole-Christoffer Granmo,
B. John Oommen:
A Hierarchy of Twofold Resource Allocation Automata Supporting Optimal Web Polling.
IEA/AIE 2008: 347-358 |
| 163 | EE | Ke Qin,
B. John Oommen:
Chaotic Pattern Recognition: The Spectrum of Properties of the Adachi Neural Network.
SSPR/SPR 2008: 540-550 |
| 162 | EE | B. John Oommen,
Dragos Calitoiu:
Modeling and simulating a disease outbreak by learning a contagion parameter-based model.
SpringSim 2008: 547-555 |
| 161 | EE | Dragos Calitoiu,
Doron Nussbaum,
B. John Oommen:
Large scale modeling of the piriform cortex for analyzing antiepileptic effects.
SpringSim 2008: 599-608 |
| 160 | EE | Dragos Calitoiu,
B. John Oommen,
Doron Nussbaum:
Spikes annihilation in the Hodgkin-Huxley neuron.
Biological Cybernetics 98(3): 239-257 (2008) |
| 159 | EE | B. John Oommen,
Sang-Woon Kim,
M. T. Samuel,
Ole-Christoffer Granmo:
A Solution to the Stochastic Point Location Problem in Metalevel Nonstationary Environments.
IEEE Transactions on Systems, Man, and Cybernetics, Part B 38(2): 466-476 (2008) |
| 158 | EE | Sang-Woon Kim,
B. John Oommen:
On Using Prototype Reduction Schemes to Optimize Kernel-Based Fisher Discriminant Analysis.
IEEE Transactions on Systems, Man, and Cybernetics, Part B 38(2): 564-570 (2008) |
| 157 | EE | Luis Rueda,
B. John Oommen:
An efficient compression scheme for data communication which uses a new family of self-organizing binary search trees.
Int. J. Communication Systems 21(10): 1091-1120 (2008) |
| 2007 |
|---|
| 156 | EE | Sudip Misra,
B. John Oommen:
The Pursuit Automaton Approach for Estimating All-Pairs Shortest Paths in Dynamically Changing Networks.
AINA Workshops (1) 2007: 124-129 |
| 155 | EE | Dragos Calitoiu,
B. John Oommen,
Doron Nussbaum:
Some Analysis on the Network of Bursting Neurons: Quantifying Behavioral Synchronization.
Australian Conference on Artificial Intelligence 2007: 110-119 |
| 154 | EE | Ole-Christoffer Granmo,
B. John Oommen:
On Using a Hierarchy of Twofold Resource Allocation Automata to Solve Stochastic Nonlinear Resource Allocation Problems.
Australian Conference on Artificial Intelligence 2007: 36-47 |
| 153 | EE | Dragos Calitoiu,
B. John Oommen,
Doron Nussbaum:
Numerical Results on the Hodgkin-Huxley Neural Network: Spikes Annihilation.
BVAI 2007: 378-387 |
| 152 | EE | Dragos Calitoiu,
B. John Oommen,
Doron Nussbaum:
Analytic Results on the Hodgkin-Huxley Neural Network: Spikes Annihilation.
Canadian Conference on AI 2007: 320-331 |
| 151 | EE | B. John Oommen,
Ebaa Fayyoumi:
A Novel Method for Micro-Aggregation in Secure Statistical Databases Using Association and Interaction.
ICICS 2007: 126-140 |
| 150 | EE | M. Khaled Hashem,
B. John Oommen:
On Using Learning Automata to Model a Student's Behavior in a Tutorial-like System.
IEA/AIE 2007: 813-822 |
| 149 | EE | B. John Oommen,
Sang-Woon Kim,
Mathew Samuel,
Ole-Christoffer Granmo:
Stochastic Point Location in Non-stationary Environments and Its Applications.
IEA/AIE 2007: 845-854 |
| 148 | EE | M. Khaled Hashem,
B. John Oommen:
Using learning automata to model a student-classroom interaction in a tutorial-like system.
SMC 2007: 1177-1182 |
| 147 | EE | M. Khaled Hashem,
B. John Oommen:
Using learning automata to model the behavior of a teacher in a tutorial-like system.
SMC 2007: 76-82 |
| 146 | EE | Abdelrahman Amer,
B. John Oommen:
A Novel Framework for Self-Organizing Lists in Environments with Locality of Reference: Lists-on-Lists.
Comput. J. 50(2): 186-196 (2007) |
| 145 | EE | B. John Oommen,
Sudip Misra,
Ole-Christoffer Granmo:
Routing Bandwidth-Guaranteed Paths in MPLS Traffic Engineering: A Multiple Race Track Learning Approach.
IEEE Trans. Computers 56(7): 959-976 (2007) |
| 144 | EE | Ole-Christoffer Granmo,
B. John Oommen,
Svein Arild Myrer,
Morten Goodwin Olsen:
Learning Automata-Based Solutions to the Nonlinear Fractional Knapsack Problem With Applications to Optimal Resource Allocation.
IEEE Transactions on Systems, Man, and Cybernetics, Part B 37(1): 166-175 (2007) |
| 143 | EE | Dragos Calitoiu,
B. John Oommen,
Doron Nussbaum:
Desynchronizing a Chaotic Pattern Recognition Neural Network to Model Inaccurate Perception.
IEEE Transactions on Systems, Man, and Cybernetics, Part B 37(3): 692-704 (2007) |
| 142 | EE | B. John Oommen,
Ghada Hany Badr:
Breadth-first search strategies for trie-based syntactic pattern recognition.
Pattern Anal. Appl. 10(1): 1-13 (2007) |
| 141 | EE | Dragos Calitoiu,
B. John Oommen,
Doron Nussbaum:
Periodicity and stability issues of a chaotic pattern recognition neural network.
Pattern Anal. Appl. 10(3): 175-188 (2007) |
| 140 | EE | Sang-Woon Kim,
B. John Oommen:
On using prototype reduction schemes to optimize dissimilarity-based classification.
Pattern Recognition 40(11): 2946-2957 (2007) |
| 139 | EE | B. John Oommen,
Sang-Woon Kim,
Geir Horn:
On the estimation of independent binomial random variables using occurrence and sequential information.
Pattern Recognition 40(11): 3263-3276 (2007) |
| 138 | EE | Pradeep K. Atrey,
Mohan S. Kankanhalli,
B. John Oommen:
Goal-oriented optimal subset selection of correlated multimedia streams.
TOMCCAP 3(1): (2007) |
| 2006 |
|---|
| 137 | EE | Ebaa Fayyoumi,
B. John Oommen:
On Optimizing the k-Ward Micro-aggregation Technique for Secure Statistical Databases.
ACISP 2006: 324-335 |
| 136 | EE | Xavier Hilaire,
B. John Oommen:
The averaged mappings problem: statement, applications, and approximate solution.
ACM Southeast Regional Conference 2006: 24-29 |
| 135 | | Denis V. Batalov,
B. John Oommen:
Turning Lights Out with DQ-Learning.
Artificial Intelligence and Applications 2006: 451-456 |
| 134 | EE | B. John Oommen,
Ole-Christoffer Granmo,
Asle Pedersen:
Empirical Verification of a Strategy for Unbounded Resolution in Finite Player Goore Games.
Australian Conference on Artificial Intelligence 2006: 1252-1258 |
| 133 | | B. John Oommen,
Jing Chen:
On Utilizing Attribute Cardinality Maps to Enhance Query Optimization in the Oracle Database System.
ICEIS (1) 2006: 23-35 |
| 132 | EE | B. John Oommen,
Jing Chen:
On Enhancing Query Optimization in the Oracle Database System by Utilizing Attribute Cardinality Maps.
ICEIS (Selected Papers) 2006: 38-71 |
| 131 | EE | Sang-Woon Kim,
B. John Oommen:
On Optimizing Dissimilarity-Based Classification Using Prototype Reduction Schemes.
ICIAR (1) 2006: 15-28 |
| 130 | EE | B. John Oommen,
Sudip Misra,
Ole-Christoffer Granmo:
A Stochastic Random-Races Algorithm for Routing in MPLS Traffic Engineering.
INFOCOM 2006 |
| 129 | EE | Ebaa Fayyoumi,
B. John Oommen:
A Fixed Structure Learning Automaton Micro-aggregation Technique for Secure Statistical Databases.
Privacy in Statistical Databases 2006: 114-128 |
| 128 | EE | B. John Oommen,
Sang-Woon Kim,
Geir Horn:
On the Theory and Applications of Sequence Based Estimation of Independent Binomial Random Variables.
SSPR/SPR 2006: 8-21 |
| 127 | EE | Sang-Woon Kim,
B. John Oommen:
On Optimizing Kernel-Based Fisher Discriminant Analysis Using Prototype Reduction Schemes.
SSPR/SPR 2006: 826-834 |
| 126 | EE | Abdelrahman Amer,
B. John Oommen:
Lists on Lists: A Framework for Self-organizing Lists in Environments with Locality of Reference.
WEA 2006: 109-120 |
| 125 | EE | Sudip Misra,
B. John Oommen:
An Efficient Dynamic Algorithm for Maintaining All-Pairs Shortest Paths in Stochastic Networks.
IEEE Trans. Computers 55(6): 686-702 (2006) |
| 124 | EE | Luis Rueda,
B. John Oommen:
A fast and efficient nearly-optimal adaptive Fano coding scheme.
Inf. Sci. 176(12): 1656-1683 (2006) |
| 123 | EE | Ghada Badr,
B. John Oommen:
A novel look-ahead optimization strategy for trie-based approximate string matching.
Pattern Anal. Appl. 9(2-3): 177-187 (2006) |
| 122 | EE | Sang-Woon Kim,
B. John Oommen:
Prototype reduction schemes applicable for non-stationary data sets.
Pattern Recognition 39(2): 209-222 (2006) |
| 121 | EE | B. John Oommen,
Luis Rueda:
Stochastic learning-based weak estimation of multinomial random variables and its applications to pattern recognition in non-stationary environments.
Pattern Recognition 39(3): 328-341 (2006) |
| 2005 |
|---|
| 120 | EE | Ghada Badr,
B. John Oommen:
On using conditional rotations and randomized heuristics for self-organizing ternary search tries.
ACM Southeast Regional Conference (1) 2005: 109-115 |
| 119 | EE | Sang-Woon Kim,
B. John Oommen:
Time-Varying Prototype Reduction Schemes Applicable for Non-stationary Data Sets.
Australian Conference on Artificial Intelligence 2005: 614-623 |
| 118 | EE | Dragos Calitoiu,
B. John Oommen,
Doron Nussbaum:
Neural Network-Based Chaotic Pattern Recognition - Part 2: Stability and Algorithmic Issues.
CORES 2005: 3-16 |
| 117 | EE | Ghada Badr,
B. John Oommen:
A Look-Ahead Branch and Bound Pruning Scheme for Trie-Based Approximate String Matching.
CORES 2005: 87-94 |
| 116 | EE | Ghada Badr,
B. John Oommen:
Enhancing Trie-Based Syntactic Pattern Recognition Using AI Heuristic Search Strategies.
ICAPR (1) 2005: 1-17 |
| 115 | EE | Geir Horn,
B. John Oommen:
A Fixed-Structure Learning Automaton Solution to the Stochastic Static Mapping Problem.
IPDPS 2005 |
| 114 | EE | Sudip Misra,
B. John Oommen:
New Algorithms for Maintaining All-Pairs Shortest Paths.
ISCC 2005: 116-121 |
| 113 | EE | Luís G. Rueda,
B. John Oommen:
Efficient Adaptive Data Compression Using Fano Binary Search Trees.
ISCIS 2005: 768-779 |
| 112 | EE | B. John Oommen,
Luís G. Rueda:
On Utilizing Stochastic Learning Weak Estimators for Training and Classification of Patterns with Non-stationary Distributions.
KI 2005: 107-120 |
| 111 | EE | Dragos Calitoiu,
B. John Oommen,
Dorin Nusbaumm:
Modeling Inaccurate Perception: Desynchronization Issues of a Chaotic Pattern Recognition Neural Network.
SCIA 2005: 821-830 |
| 110 | EE | B. John Oommen,
Luís G. Rueda:
A formal analysis of why heuristic functions work.
Artif. Intell. 164(1-2): 1-22 (2005) |
| 109 | EE | Ghada Hany Badr,
B. John Oommen:
Self-Adjusting of Ternary Search Tries Using Conditional Rotations and Randomized Heuristics.
Comput. J. 48(2): 200-219 (2005) |
| 108 | EE | Sang-Woon Kim,
B. John Oommen:
On Utilizing Search Methods to Select Subspace Dimensions for Kernel-Based Nonlinear Subspace Classifiers.
IEEE Trans. Pattern Anal. Mach. Intell. 27(1): 136-141 (2005) |
| 107 | EE | Sang-Woon Kim,
B. John Oommen:
On Using Prototype Reduction Schemes and Classifier Fusion Strategies to Optimize Kernel-Based Nonlinear Subspace Methods.
IEEE Trans. Pattern Anal. Mach. Intell. 27(3): 455-460 (2005) |
| 106 | EE | Sudip Misra,
B. John Oommen:
Dynamic algorithms for the shortest path routing problem: learning automata-based solutions.
IEEE Transactions on Systems, Man, and Cybernetics, Part B 35(6): 1179-1192 (2005) |
| 2004 |
|---|
| 105 | | Sudip Misra,
B. John Oommen:
Adaptive Algorithms for Routing and Traffic Engineering in Stochastic Networks.
AAAI 2004: 993-994 |
| 104 | EE | B. John Oommen,
Jack R. Zgierski,
Doron Nussbaum:
Deterministic Majority filters applied to stochastic sorting.
ACM Southeast Regional Conference 2004: 228-233 |
| 103 | EE | Luís G. Rueda,
B. John Oommen:
On Families of New Adaptive Compression Algorithms Suitable for Time-Varying Source Data.
ADVIS 2004: 234-244 |
| 102 | EE | Sang-Woon Kim,
B. John Oommen:
Selecting Subspace Dimensions for Kernel-Based Nonlinear Subspace Classifiers Using Intelligent Search Methods.
Australian Conference on Artificial Intelligence 2004: 1115-1121 |
| 101 | EE | Sudip Misra,
B. John Oommen:
Stochastic Learning Automata-Based Dynamic Algorithms for the Single Source Shortest Path Problem.
IEA/AIE 2004: 239-248 |
| 100 | EE | Sudip Misra,
B. John Oommen:
Generalized pursuit learning algorithms for shortest path routing tree computation.
ISCC 2004: 891-896 |
| 99 | | B. John Oommen,
Jack R. Zgierski,
Doron Nussbaum:
Stochastic Sorting Using Deterministic Consecutive and Leader Filters.
MSV/AMCS 2004: 399-405 |
| 98 | | Qun Wang,
B. John Oommen:
On Designing Pattern Classifiers Using Artificially Created Bootstrap Samples.
PRIS 2004: 159-168 |
| 97 | | B. John Oommen:
Recent Results on Learning from Stochastic Teachers and Compulsive Liars/Con-Men.
PRIS 2004: 4 |
| 96 | EE | B. John Oommen,
Ghada Badr:
Dictionary-Based Syntactic Pattern Recognition Using Tries.
SSPR/SPR 2004: 251-259 |
| 95 | EE | B. John Oommen,
Luís G. Rueda:
A New Family of Weak Estimators for Training in Non-stationary Distributions.
SSPR/SPR 2004: 644-652 |
| 94 | EE | Sang-Woon Kim,
B. John Oommen:
Enhancing prototype reduction schemes with recursion: a method applicable for "large" data sets.
IEEE Transactions on Systems, Man, and Cybernetics, Part B 34(3): 1384-1397 (2004) |
| 93 | EE | Luís G. Rueda,
B. John Oommen:
A nearly-optimal Fano-based coding algorithm.
Inf. Process. Manage. 40(2): 257-268 (2004) |
| 92 | EE | M. Ouerd,
B. John Oommen,
Stan Matwin:
A formal approach to using data distributions for building causal polytree structures.
Inf. Sci. 168(1-4): 111-132 (2004) |
| 91 | EE | Sang-Woon Kim,
B. John Oommen:
On using prototype reduction schemes to optimize kernel-based nonlinear subspace methods.
Pattern Recognition 37(2): 227-239 (2004) |
| 2003 |
|---|
| 90 | EE | B. John Oommen,
Govindachari Raghunath,
Benjamin Kuipers:
On How to Learn from a Stochastic Teacher or a Stochastic Compulsive Liar of Unknown Identity.
Australian Conference on Artificial Intelligence 2003: 24-40 |
| 89 | EE | Sang-Woon Kim,
B. John Oommen:
On Using Prototype Reduction Schemes and Classifier Fusion Strategies to Optimize Kernel-Based Nonlinear Subspace Methods.
Australian Conference on Artificial Intelligence 2003: 783-795 |
| 88 | EE | Ouerd Messaouda,
B. John Oommen,
Stan Matwin:
Enhancing Caching in Distributed Databases Using Intelligent Polytree Representations.
Canadian Conference on AI 2003: 498-504 |
| 87 | EE | B. John Oommen,
Jing Chen:
A new histogram method for sparse attributes: the averaged rectangular attribute cardinality map.
ISICT 2003: 119-125 |
| 86 | | Qun Wang,
B. John Oommen:
Classification Error-Rate Estimation Using New Pseudo-Sample Bootstrap Methods.
PRIS 2003: 96-103 |
| 85 | | B. John Oommen,
Murali Thiyagarajah:
Benchmarking attribute cardinality maps for database systems using the TPC-D specifications.
IEEE Transactions on Systems, Man, and Cybernetics, Part B 33(6): 913-924 (2003) |
| 84 | EE | Sang-Woon Kim,
B. John Oommen:
A brief taxonomy and ranking of creative prototype reduction schemes.
Pattern Anal. Appl. 6(3): 232-244 (2003) |
| 83 | EE | Luís G. Rueda,
B. John Oommen:
On optimal pairwise linear classifiers for normal distributions: the d-dimensional case.
Pattern Recognition 36(1): 13-23 (2003) |
| 82 | EE | Sang-Woon Kim,
B. John Oommen:
Enhancing prototype reduction schemes with LVQ3-type algorithms.
Pattern Recognition 36(5): 1083-1093 (2003) |
| 2002 |
|---|
| 81 | EE | Sang-Woon Kim,
B. John Oommen:
Optimizing Kernel-Based Nonlinear Subspace Methods Using Prototype Reduction Schemes.
Australian Joint Conference on Artificial Intelligence 2002: 155-166 |
| 80 | | Sang-Woon Kim,
B. John Oommen:
On Utilizing LVQ3-Type Algorithms to Enhance Prototype Reduction Schemes.
PRIS 2002: 242-256 |
| 79 | | B. John Oommen,
Luís G. Rueda:
Using Pattern Recognition Techniques to Derive a Formal Analysis of Why Heuristics Functions Work.
PRIS 2002: 45-58 |
| 78 | EE | Sang-Woon Kim,
B. John Oommen:
Recursive Prototype Reduction Schemes Applicable for Large Data Sets.
SSPR/SPR 2002: 528-537 |
| 77 | EE | B. John Oommen,
Luís G. Rueda:
The Efficiency of Histogram-like Techniques for Database Query Optimization.
Comput. J. 45(5): 494-510 (2002) |
| 76 | EE | Luís G. Rueda,
B. John Oommen:
On Optimal Pairwise Linear Classifiers for Normal Distributions: The Two-Dimensional Case.
IEEE Trans. Pattern Anal. Mach. Intell. 24(2): 274-280 (2002) |
| 75 | | M. Agache,
B. John Oommen:
Generalized pursuit learning schemes: new families of continuous and discretized learning automata.
IEEE Transactions on Systems, Man, and Cybernetics, Part B 32(6): 738-749 (2002) |
| 74 | | B. John Oommen,
T. Dale Roberts:
Discretized learning automata solutions to the capacity assignment problem for prioritized networks.
IEEE Transactions on Systems, Man, and Cybernetics, Part B 32(6): 821-831 (2002) |
| 73 | EE | Gopal Racherla,
Sridhar Radhakrishnan,
B. John Oommen:
Enhanced layered segment trees: a pragmatic data structure for real-time processing of geometric objects.
Pattern Recognition 35(10): 2303-2309 (2002) |
| 2001 |
|---|
| 72 | EE | Luís G. Rueda,
B. John Oommen:
Resolving Minsky's Paradox: The d-Dimensional Normal Distribution Case.
Australian Joint Conference on Artificial Intelligence 2001: 25-36 |
| 71 | | B. John Oommen,
Luís G. Rueda:
Histogram Methods in Query Optimization: The Relation between Accuracy and Optimality.
DASFAA 2001: 320-326 |
| 70 | EE | Gopal Racherla,
Sridhar Radhakrishnan,
B. John Oommen:
A New Geometric Tool for Pattern Recognition - An Algorithm for Real Time Insertion of Layered Segment Trees.
ICAPR 2001: 212-221 |
| 69 | | B. John Oommen,
Qun Wang:
Distance Bias Adjustment Bootstrap Estimation for Bhattacharyya Error Bound in Classifiers.
PRIS 2001: 103-117 |
| 68 | EE | B. John Oommen,
R. K. S. Loke:
On the Pattern Recognition of Noisy Subsequence Trees.
IEEE Trans. Pattern Anal. Mach. Intell. 23(9): 929-946 (2001) |
| 67 | | B. John Oommen,
M. Agache:
Continuous and discretized pursuit learning schemes: various algorithms and their comparison.
IEEE Transactions on Systems, Man, and Cybernetics, Part B 31(3): 277-287 (2001) |
| 2000 |
|---|
| 66 | | Necati Aras,
I. Kuban Altinel,
B. John Oommen:
A Kohonen-like Decomposition Method for the Traveling Salesman Problem: KNIESDECOMPOSE.
ECAI 2000: 261-265 |
| 65 | | B. John Oommen,
Luis Rueda:
An Empirical Comparison of Histogram-Like Techniques for Query Optimization.
ICEIS 2000: 71-78 |
| 64 | EE | B. John Oommen,
Murali Thiyagarajah:
Query Result Size Estimation Using the Trapezoidal Attribute Cardinality Map.
IDEAS 2000: 236-242 |
| 63 | EE | M. Ouerd,
B. John Oommen,
Stan Matwin:
A Formalism for Building Causal Polytree Structures Using Data Distributions.
ISMIS 2000: 629-637 |
| 62 | EE | Luis Rueda,
B. John Oommen:
The Foundational Theory of Optimal Bayesian Pairwise Linear Classifiers.
SSPR/SPR 2000: 581-590 |
| 61 | EE | B. John Oommen,
T. Dale Roberts:
Continuous Learning Automata Solutions to the Capacity Assignment Problem.
IEEE Trans. Computers 49(6): 608-620 (2000) |
| 1999 |
|---|
| 60 | EE | Murali Thiyagarajah,
B. John Oommen:
On Benchmarking Attribute Cardinality Maps for Database Systems Using the TPC-D Specification.
DEXA 1999: 292-301 |
| 59 | | Murali Thiyagarajah,
B. John Oommen:
Prototype Validation of the Trapezoidal Attribute Cardinality Map for Query Optimization in Database Systems.
ICEIS 1999: 156-162 |
| 58 | EE | B. John Oommen,
Murali Thiyagarajah:
Query Result Size Estimation Using a Novel Histogram-like Technique: The Rectangular Attribute Cardinality Map.
IDEAS 1999: 3-15 |
| 57 | | B. John Oommen,
T. Dale Roberts:
On Solving the Capacity Assignment Problem Using Continous Learning Automata.
IEA/AIE 1999: 622-631 |
| 56 | | B. John Oommen,
R. K. S. Loke:
Designing syntactic pattern classifiers using vector quantization and parametric string editing.
IEEE Transactions on Systems, Man, and Cybernetics, Part B 29(6): 881-888 (1999) |
| 55 | EE | Necati Aras,
B. John Oommen,
I. Kuban Altinel:
The Kohonen network incorporating explicit statistics and its application to the travelling salesman problem.
Neural Networks 12(9): 1273-1284 (1999) |
| 1998 |
|---|
| 54 | | B. John Oommen,
T. Dale Roberts:
A Fast Efficient Solution to the Capacity Assignment Problem Using Discretized Learning Automata.
IEA/AIE (Vol. 2) 1998: 56-65 |
| 53 | | B. John Oommen,
R. K. S. Loke:
The Noisy Subsequence Tree Recognition Problem.
SSPR/SPR 1998: 169-180 |
| 52 | | B. John Oommen,
I. Kuban Altinel,
Necati Aras:
Discrete vector quantization for arbitrary distance function estimation.
IEEE Transactions on Systems, Man, and Cybernetics, Part B 28(4): 496-510 (1998) |
| 51 | | B. John Oommen,
Govindachari Raghunath:
Automata learning and intelligent tertiary searching for stochastic point location.
IEEE Transactions on Systems, Man, and Cybernetics, Part B 28(6): 947-954 (1998) |
| 50 | EE | B. John Oommen,
Rangasami L. Kashyap:
A formal theory for optimal and information theoretic syntactic pattern recognition.
Pattern Recognition 31(8): 1159-1177 (1998) |
| 1997 |
|---|
| 49 | | B. John Oommen,
Juan Dong:
Generalized Swap-with-Parent Schemes for Self-Organizing Sequential Linear Lists.
ISAAC 1997: 414-423 |
| 48 | EE | Qingxin Zhu,
B. John Oommen:
On the Optimal Search Problem: The Case when the Target Distribution is Unknown.
SCCC 1997: 268-277 |
| 47 | EE | Thai B. Nguyen,
B. John Oommen:
Moment-Preserving Piecewise Linear Approximations of Signals and Images.
IEEE Trans. Pattern Anal. Mach. Intell. 19(1): 84-91 (1997) |
| 46 | | B. John Oommen,
Edward V. de St. Croix:
String taxonomy using learning automata.
IEEE Transactions on Systems, Man, and Cybernetics, Part B 27(2): 354-365 (1997) |
| 45 | | B. John Oommen:
Stochastic searching on the line and its applications to parameter learning in nonlinear optimization.
IEEE Transactions on Systems, Man, and Cybernetics, Part B 27(4): 733-739 (1997) |
| 44 | EE | I. Kuban Altinel,
B. John Oommen,
Necati Aras:
Vector Quantization for Arbitrary Distance Function Estimation.
INFORMS Journal on Computing 9(4): 439-451 (1997) |
| 43 | EE | B. John Oommen,
Richard K. S. Loke:
Pattern recognition of strings with substitutions, insertions, deletions and generalized transpositions.
Pattern Recognition 30(5): 789-800 (1997) |
| 1996 |
|---|
| 42 | | B. John Oommen,
R. K. S. Loke:
Optimal and Information Theoretic Syntactic Pattern Recognition Involving Traditional and Transposition Errors.
FSTTCS 1996: 224-237 |
| 41 | | B. John Oommen,
Rangasami L. Kashyap:
Optimal and Information Theoretic Syntactic Pattern Recognition for Traditional Errors.
SSPR 1996: 11-20 |
| 40 | | B. John Oommen,
K. Zhang,
William Lee:
Numerical Similarity and Dissimilarity Measures Between Two Trees.
IEEE Trans. Computers 45(12): 1426-1434 (1996) |
| 39 | | B. John Oommen,
Edward V. de St. Croix:
Graph Partitioning Using Learning Automata.
IEEE Trans. Computers 45(2): 195-208 (1996) |
| 38 | EE | B. John Oommen,
K. Zhang:
The Normalized String Editing Problem Revisited.
IEEE Trans. Pattern Anal. Mach. Intell. 18(6): 669-672 (1996) |
| 1995 |
|---|
| 37 | | B. John Oommen,
R. K. S. Loke:
Noisy Subsequence Recognition Using Constrained String Editing Involving Substitutions, Insertions, Deletions and Generalized Transpositions.
ICSC 1995: 116-123 |
| 36 | | B. John Oommen,
Edward V. de St. Croix:
On Using Learning Automata for Fast Graph Partitioning.
LATIN 1995: 449-460 |
| 35 | | B. John Oommen:
String Alignment with Substitution, Insertion, Deletion, Squashing and Expansion Operations.
Inf. Sci. 83(1&2): 89-107 (1995) |
| 1994 |
|---|
| 34 | | B. John Oommen,
David T. H. Ng:
A New Technique for Enhancing Linked-List Data Retrieval: Reorganize Data Using Artificially synthesized Queries.
Comput. J. 37(7): 598-609 (1994) |
| 33 | | B. John Oommen,
William Lee:
Constrained Tree Editing.
Inf. Sci. 77(3-4): 253-273 (1994) |
| 1993 |
|---|
| 32 | | B. John Oommen:
Transforming Ill-Conditioned Constrained Problems using Projections.
Comput. J. 36(3): 282-285 (1993) |
| 31 | | B. John Oommen,
Chris Fothergill:
Fast Learning Automaton-Based Image Examination and Retrieval.
Comput. J. 36(6): 542-553 (1993) |
| 30 | EE | Robert P. Cheetham,
B. John Oommen,
David T. H. Ng:
Adaptive Structuring of Binary Search Trees Using Conditional Rotations.
IEEE Trans. Knowl. Data Eng. 5(4): 695-704 (1993) |
| 29 | EE | B. John Oommen,
Jack R. Zgierski:
Breaking Substitution Cyphers Using Stochastic Automata.
IEEE Trans. Pattern Anal. Mach. Intell. 15(2): 185-192 (1993) |
| 28 | | Radhakrishna S. Valiveti,
B. John Oommen:
Self-Organizing Doubly-Linked Lists.
J. Algorithms 14(1): 88-114 (1993) |
| 27 | EE | Radhakrishna S. Valiveti,
B. John Oommen:
Determining stochastic dependence for normally distributed vectors using the chi-squared metric.
Pattern Recognition 26(6): 975-987 (1993) |
| 26 | | B. John Oommen,
David T. H. Ng:
An Optimal Absorbing List Organization Strategy with Constant Memory Requirements.
Theor. Comput. Sci. 119(2): 355-361 (1993) |
| 1992 |
|---|
| 25 | | David T. H. Ng,
B. John Oommen:
A Short Note on Doubly-Linked List Reorganizing Heuristics.
Comput. J. 35(5): 533-535 (1992) |
| 24 | EE | B. John Oommen,
I. Reichstein:
On the problem of multiple mobile robots cluttering a workspace.
Inf. Sci. 63(1-2): 55-85 (1992) |
| 23 | EE | Radhakrishna S. Valiveti,
B. John Oommen:
On using the chi-squared metric for determining stochastic dependence.
Pattern Recognition 25(11): 1389-1400 (1992) |
| 1991 |
|---|
| 22 | | Radhakrishna S. Valiveti,
B. John Oommen,
Jack R. Zgierski:
Adaptive Linear List Reorganization for a System Processing Set Queries.
FCT 1991: 405-414 |
| 21 | EE | Radhakrishna S. Valiveti,
B. John Oommen:
Recognizing Sources of Random Strings.
IEEE Trans. Pattern Anal. Mach. Intell. 13(4): 386-394 (1991) |
| 1990 |
|---|
| 20 | EE | B. John Oommen,
Radhakrishna S. Valiveti,
Jack R. Zgierski:
A Fast Learning Automaton Solution to the Keyboard Optimization Problem.
IEA/AIE (Vol. 2) 1990: 981-990 |
| 19 | | B. John Oommen,
David T. H. Ng:
On Generating Random Permutations with Arbitrary Distributions.
Comput. J. 33(4): 368-374 (1990) |
| 18 | | B. John Oommen,
E. R. Hansen,
J. Ian Munro:
Deterministic Optimal and Expedient Move-to-Rear List Organizing Strategies.
Theor. Comput. Sci. 74(2): 183-197 (1990) |
| 1989 |
|---|
| 17 | | B. John Oommen,
David T. H. Ng:
On Generating Random Permutations with Arbitrary Distributions.
ACM Conference on Computer Science 1989: 27-32 |
| 16 | | David T. H. Ng,
B. John Oommen:
Generalizing Singly-Linked List Reorganizing Heuristics for Doubly-Linked Lists.
MFCS 1989: 380-389 |
| 15 | | B. John Oommen,
David T. H. Ng:
Optimal Constant Space Move-to-Rear List Organization.
Optimal Algorithms 1989: 115-125 |
| 1988 |
|---|
| 14 | EE | Robert P. Cheetham,
B. John Oommen,
David T. H. Ng:
On Using Conditional Rotation Operations to Adaptively Structure Binary Search Trees.
ICDT 1988: 161-175 |
| 13 | | B. John Oommen,
Daniel C. Y. Ma:
Deterministic Learning Automata Solutions to the Equipartitioning Problem.
IEEE Trans. Computers 37(1): 2-13 (1988) |
| 12 | EE | B. John Oommen:
Correction to "Recognition of Noisy Subsequences Using Constrained Edit Distances".
IEEE Trans. Pattern Anal. Mach. Intell. 10(6): 983-984 (1988) |
| 1987 |
|---|
| 11 | EE | B. John Oommen,
Daniel C. Y. Ma:
Fast Object Partitioning Using Stochastic Learning Automata.
SIGIR 1987: 111-122 |
| 10 | | B. John Oommen,
E. R. Hansen:
List Organizing Strategies Using Stochastic Move-to-Front and Stochastic Move-to-Rear Operations.
SIAM J. Comput. 16(4): 705-716 (1987) |
| 1986 |
|---|
| 9 | | B. John Oommen,
S. Sitharama Iyengar,
Nageswara S. V. Rao,
Rangasami L. Kashyap:
Robot Navigation in Unknown Terrains of Convex Polygonal Obstacles Using Learned Visibility Graphs.
AAAI 1986: 1101-1106 |
| 8 | EE | B. John Oommen,
E. R. Hansen:
Expedient Stochastic Move-to-Front and optimal Move-to-Rear List Organizing Strategies.
ICDT 1986: 349-364 |
| 7 | EE | B. John Oommen:
Constrained string editing.
Inf. Sci. 40(3): 267-284 (1986) |
| 1985 |
|---|
| 6 | | B. John Oommen:
On the Futility of Arbitrarily Increasing Memory Capabilities of Stochastic Learning Automata.
CAIA 1985: 308-312 |
| 5 | EE | B. John Oommen,
M. A. L. Thathachar:
Multiaction learning automata possessing ergodicity of the mean.
Inf. Sci. 35(3): 183-198 (1985) |
| 1984 |
|---|
| 4 | | B. John Oommen:
Algorithms for String Editing which Permit Arbitrarily Complex Editing Constraints.
MFCS 1984: 443-451 |
| 1983 |
|---|
| 3 | | Rangasami L. Kashyap,
B. John Oommen:
The Noisy Substring Matching Problem.
IEEE Trans. Software Eng. 9(3): 365-370 (1983) |
| 1981 |
|---|
| 2 | EE | Rangasami L. Kashyap,
B. John Oommen:
An effective algorithm for string correction using generalized edit distances--I. Description of the algorithm and its optimality.
Inf. Sci. 23(2): 123-142 (1981) |
| 1 | EE | Rangasami L. Kashyap,
B. John Oommen:
An effective algorithm for string correction using generalized edit distance - II. Computational complexity of the algorithm and some applications.
Inf. Sci. 23(3): 201-217 (1981) |
