Blar i AURA på forfatter "Zhang, Xuan"

A formal proof of the eoptimality of discretized pursuit algorithms
Zhang, Xuan; Oommen, John; Granmo, OleChristoffer; Lei, Jiao (Journal article; Peer reviewed, 2015) 
A formal proof of the εoptimality of absorbing continuous pursuit algorithms using the theory of regular functions
Zhang, Xuan; Granmo, OleChristoffer; Oommen, B. John; Jiao, Lei (Journal article; Peer reviewed, 2014)The most difficult part in the design and analysis of Learning Automata (LA) consists of the formal proofs of their convergence accuracies. The mathematical techniques used for the different families (Fixed Structure, ... 
Building Concise Logical Patterns by Constraining Tsetlin Machine Clause Size
Abeyrathna, Kuruge Darshana; Abouzeid, Ahmed Abdulrahem Othman; Bhattarai, Bimal; Giri, Charul; Glimsdal, Sondre; Granmo, OleChristoffer; Lei, Jiao; Saha, Rupsa; Sharma, Jivitesh; Tunheim, Svein Anders; Zhang, Xuan (Academic article, 2023)Tsetlin machine (TM) is a logicbased machine learning approach with the crucial advantages of being transparent and hardwarefriendly. While TMs match or surpass deep learning accuracy for an increasing number of applications, ... 
A Conclusive Analysis of the FiniteTime Behavior of the Discretized Pursuit Learning Automaton
Zhang, Xuan; Jiao, Lei; Oommen, John; Granmo, OleChristoffer (Journal article; Peer reviewed, 2019)This paper deals with the finitetime analysis (FTA) of learning automata (LA), which is a topic for which very little work has been reported in the literature. This is as opposed to the asymptotic steadystate analysis ... 
Discretized Bayesian pursuit – A new scheme for reinforcement learning
Zhang, Xuan; Granmo, OleChristoffer; Oommen, B. John (Lecture Notes in Computer Science;7345, Chapter; Peer reviewed, 2012)The success of Learning Automata (LA)based estimator algorithms over the classical, Linear RewardInaction ( L RI )like schemes, can be explained by their ability to pursue the actions with the highest reward probability ... 
Generalized Bayesian pursuit: A novel scheme for multiarmed Bernoulli bandit problems
Zhang, Xuan; Oommen, B. John; Granmo, OleChristoffer (IFIP Advances in Information and Communication Technology;364, Chapter; Peer reviewed, 2011)In the last decades, a myriad of approaches to the multiarmed bandit problem have appeared in several different fields. The current top performing algorithms from the field of Learning Automata reside in the Pursuit family, ... 
The Hierarchical Continuous Pursuit Learning Automation : A Novel Scheme for Environments With Large Numbers of Actions
Yazidi, Anis; Zhang, Xuan; Lei, Jiao; Oommen, John (Journal article; Peer reviewed, 2019) 
The Hierarchical Discrete Learning Automaton Suitable for Environments with Many Actions and High Accuracy Requirements
Omslandseter, Rebekka Olsson; Jiao, Lei; Zhang, Xuan; Yazidi, Anis; Oommen, John (Peer reviewed; Journal article, 2022)Since its early beginning, the paradigm of Learning Automata (LA), has attracted much interest. Over the last decades, new concepts and various improvements have been introduced to increase the LA’s speed and accuracy, ... 
The Hierarchical Discrete Pursuit Learning Automaton: A Novel Scheme With Fast Convergence and EpsilonOptimality
Omslandseter, Rebekka Olsson; Jiao, Lei; Zhang, Xuan; Yazidi, Anis; Oommen, John (Peer reviewed; Journal article, 2022)Since the early 1960s, the paradigm of learning automata (LA) has experienced abundant interest. Arguably, it has also served as the foundation for the phenomenon and field of reinforcement learning (RL). Over the decades, ... 
On incorporating the paradigms of discretization and Bayesian estimation to create a new family of pursuit learning automata
Zhang, Xuan; Granmo, OleChristoffer; 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 the Convergence of Tsetlin Machines for the XOR Operator
Lei, Jiao; Zhang, Xuan; Granmo, OleChristoffer; Abeyrathna, Kuruge Darshana (Peer reviewed; Journal article, 2022)The Tsetlin Machine (TM) is a novel machine learning algorithm with several distinct properties, including transparent inference and learning using hardwarenear building blocks. Although numerous papers explore the TM ... 
On using the theory of regular functions to prove the εOptimality of the Continuous Pursuit Learning Automaton
Zhang, Xuan; Granmo, OleChristoffer; 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 εoptimality is defined as their ability to converge ... 
Optimization channel selection for cognitive radio networks using a distributed Bayesian learning automatabased approach
Lei, Jiao; Zhang, Xuan; Oommen, John; Granmo, OleChristoffer (Journal article; Peer reviewed, 2015) 
The regression Tsetlin machine: a novel approach to interpretable nonlinear regression
Abeyrathna, Kuruge Darshana; Granmo, OleChristoffer; Zhang, Xuan; Lei, Jiao; Goodwin, Morten (Peer reviewed; Journal article, 2019) 
The bayesian pursuit algorithm: A new family of estimator learning automata
Zhang, Xuan; Granmo, OleChristoffer; Oommen, B. John (Lecture Notes in Computer Science;6704, Chapter; Peer reviewed, 2011)The fastest Learning Automata (LA) algorithms currently available come from the family of estimator algorithms. The Pursuit algorithm (PST), a pioneering scheme in the estimator family, obtains its superior learning speed ... 
The design of absorbing Bayesian pursuit algorithms and the formal analyses of their εoptimality
Zhang, Xuan; Oommen, John; Granmo, OleChristoffer (Peer reviewed; Journal article, 2016)The fundamental phenomenon that has been used to enhance the convergence speed of learning automata (LA) is that of incorporating the running maximum likelihood (ML) estimates of the action reward probabilities into the ... 
The Hierarchical Continuous Pursuit Learning Automation for Large Numbers of Actions
Yazidi, Anis; Zhang, Xuan; Lei, Jiao; Oommen, John (Chapter; Peer reviewed, 2018) 
Tsetlin Machine for Fake News Detection: Enhancing Accuracy and Reliability
Ledaal, Bjørn Vetle (Master thesis, 2023)This thesis aims to improve the accuracy of fake news detection by using Tsetlin Machines (TM). TMs are well suited for noisy and complex relations within the provided data, which on initial analysis, overlaps nicely with ...