Blar i AURA på forfatter "Oommen, B. John"
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Learning Automaton Based On-Line Discovery and Tracking of Spatio-temporal Event Patterns
Yazidi, Anis; Granmo, Ole-Christoffer; Lin, Min; Wen, Xifeng; Oommen, B. John; Gerdes, Martin; Reichert, Frank (Lecture Notes in Computer Science; no. 6230, Journal article; Peer reviewed, 2010)Discovering and tracking of spatio-temporal patterns in noisy sequences of events is a difficult task that has become increasingly pertinent due to recent advances in ubiquitous computing, such as community-based social ... -
Modeling a Student's Behavior in a Tutorial-Like System Using Learning Automata
Oommen, B. John; Hashem, M. Khaled (Journal article; Peer reviewed, 2010)This paper presents a new philosophy to model the behavior of a student in a tutorial- like system using learning automata (LAs). The model of the student in our system is inferred using a higher level LA, referred to as ... -
Modeling a teacher in a tutorial-like system using Learning Automata
Oommen, B. John; Hashem, M. Khaled (Lecture Notes in Computer Science;7430, Chapter; Peer reviewed, 2012)The goal of this paper is to present a novel approach to model the behavior of a Teacher in a Tutorial- like system. In this model, the Teacher is capable of presenting teaching material from a Socratic-type Domain model ... -
Multi-class pairwise linear dimensionality reduction using heteroscedastic schemes
Rueda, Luis; Oommen, B. John; Henríquez, Claudio (Peer reviewed; Journal article, 2010)Linear dimensionality reduction (LDR) techniques have been increasingly important in pattern recognition (PR) due to the fact that they permit a relatively simple mapping of the problem onto a lower-dimensional subspace, ... -
Networking logistic neurons can yield chaotic and pattern recognition properties
Qin, Ke; Oommen, B. John (Chapter; Peer reviewed, 2011)Over the last few years, the field of Chaotic Neural Networks (CNNs) has been extensively studied because of their potential applications in the understanding/recognition of patterns and images, their associative memory ... -
A new tool for the modeling of AI and machine learning applications: Random walk-jump processes
Yazidi, Anis; Granmo, Ole-Christoffer; Oommen, B. John (Lecture notes in computer science;6678, Chapter; Peer reviewed, 2011)There are numerous applications in Artificial Intelligence (AI) and Machine Learning (ML) where the criteria for decisions are based on testing procedures. The most common tools used in such random phenomena involve Random ... -
On achieving near-optimal “Anti-Bayesian” Order Statistics-Based classification fora asymmetric exponential distributions
Thomas, Anu; Oommen, B. John (Lecture Notes in Computer Science;8047, Chapter; Peer reviewed, 2013)This paper considers the use of Order Statistics (OS) in the theory of Pattern Recognition (PR). The pioneering work on using OS for classification was presented in [1] for the Uniform distribution, where it was shown that ... -
On incorporating the paradigms of discretization and Bayesian estimation to create a new family of pursuit learning automata
Zhang, Xuan; Granmo, Ole-Christoffer; Oommen, B. John (Journal article; Peer reviewed, 2013)There are currently two fundamental paradigms that have been used to enhance the convergence speed of Learning Automata (LA). The first involves the concept of utilizing the estimates of the reward probabilities, while the ... -
On merging the fields of neural networks and adaptive data structures to yield new pattern recognition methodologies
Oommen, B. John (Lecture Notes in Computer Science;6744, Chapter; Peer reviewed, 2011)The aim of this talk is to explain a pioneering exploratory research endeavour that attempts to merge two completely different fields in Computer Science so as to yield very fascinating results. These are the well-established ... -
On Optimizing Locally Linear Nearest Neighbour Reconstructions Using Prototype Reduction Schemes
Kim, Sang-Woon; Oommen, B. John (Lecture Notes in Computer Science;, Chapter; Peer reviewed, 2010)This paper concerns the use of Prototype Reduction Schemes (PRS) to optimize the computations involved in typical k-Nearest Neighbor (k-NN) rules. These rules have been successfully used for decades in statistical Pattern ... -
On the analysis of a new Markov chain which has applications in AI and machine learning
Yazidi, Anis; Granmo, Ole-Christoffer; Oommen, B. John (Chapter; Peer reviewed, 2011)In this paper, we consider the analysis of a fascinating Random Walk (RW) that contains interleaving random steps and random "jumps". The characterizing aspect of such a chain is that every step is paired with its counterpart ... -
On the pattern recognition and classification of stochastically episodic events
Bellinger, Colin; Oommen, B. John (Lecture Notes in Computer Science; 7190;, Chapter; Peer reviewed, 2012)Researchers in the field of Pattern Recognition (PR) have traditionally presumed the availability of a representative set of data drawn from the classes of interest, say ω 1 and ω 2 in a 2-class problem. These samples are ... -
On using prototype reduction schemes to optimize locally linear reconstruction methods
Kim, Sang-Woon; Oommen, B. John (Journal article; Peer reviewed, 2012)This paper concerns the use of prototype reduction schemes (PRS) to optimize the computations involved in typical k-nearest neighbor (k-NN) rules. These rules have been successfully used for decades in statistical pattern ... -
On using the theory of regular functions to prove the ε-Optimality of the Continuous Pursuit Learning Automaton
Zhang, Xuan; Granmo, Ole-Christoffer; Oommen, B. John; Jiao, Lei (Lecture Notes in Computer Science;7906, Chapter; Peer reviewed, 2013)There are various families of Learning Automata (LA) such as Fixed Structure, Variable Structure, Discretized etc. Informally, if the environment is stationary, their ε-optimality is defined as their ability to converge ... -
On Utilizing Association and Interaction Concepts for Enhancing Microaggregation in Secure Statistical Databases
Oommen, B. John; Fayyoumi, Ebaa (Journal article; Peer reviewed, 2010)This paper presents a possibly pioneering endeavor to tackle the microaggregation techniques (MATs) in secure statistical databases by resorting to the principles of associative neural networks (NNs). The prior art has ... -
On utilizing dependence-based information to enhance micro-aggregation for secure statistical databases
Oommen, B. John; Fayyoumi, Ebaa (Journal article; Peer reviewed, 2013)We consider the micro-aggregation problem which involves partitioning a set of individual records in a micro-data file into a number of mutually exclusive and exhaustive groups. This problem, which seeks for the best ... -
Optimal sampling for estimation with constrained resources using a learning automaton-based solution for the nonlinear fractional knapsack problem
Granmo, Ole-Christoffer; Oommen, B. John (Journal article; Peer reviewed, 2010)While training and estimation for Pattern Recognition (PR) have been extensively studied, the question of achieving these when the resources are both limited and constrained is relatively open. This is the focus of this ... -
Optimal “anti-Bayesian” parametric pattern classification for the exponential family using Order Statistics criteria
Thomas, A.; Oommen, B. John (Lecture Notes in Computer Science;7324, Chapter; Peer reviewed, 2012)This paper reports some pioneering results in which optimal parametric classification is achieved in a counter-intuitive manner, quite opposed to the Bayesian paradigm. The paper, which builds on the results of [1], ... -
Optimal “anti-Bayesian” parametric pattern classification using Order Statistics criteria
Thomas, A.; Oommen, B. John (Lecture Notes in Computer Science;7441, Chapter; Peer reviewed, 2012)The gold standard for a classifier is the condition of optimality attained by the Bayesian classifier. Within a Bayesian paradigm, if we are allowed to compare the testing sample with only a single point in the feature ... -
Peptide classification using optimal and information theoretic syntactic modeling
Aygün, Ezra; Oommen, B. John; Cataltepe, Z (Journal article; Peer reviewed, 2010)We consider the problem of classifying peptides using the information residing in their syntactic representations. This problem, which has been studied for more than a decade, has typically been investigated using ...