Xuan Zhang

The Hierarchical Discrete Pursuit Learning Automaton: A Novel Scheme With Fast Convergence and Epsilon-Optimality – IEEE Transactions on Neural Networks and Learning Systems 2022

The Hierarchical Discrete Learning Automaton Suitable for Environments with Many Actions and High Accuracy Requirements – Lecture Notes in Computer Science (LNCS) 2022

A Comprehensive Survey of Estimator Learning Automata and Their Recent Convergence Results – Lecture Notes in Networks and Systems 2022

On the Convergence of Tsetlin Machines for the XOR Operator – IEEE Transactions on Pattern Analysis and Machine Intelligence 2022

Contrastive autoencoder for anomaly detection in multivariate time series – Information Sciences 2022

On the convergence of Tsetlin machines for the IDENTITY- and NOT operators – IEEE Transactions on Pattern Analysis and Machine Intelligence 2021

Adaptive Continuous Feature Binarization for Tsetlin Machines Applied to Forecasting Dengue Incidences in the Philippines – 2020

The regression Tsetlin machine: a novel approach to interpretable nonlinear regression – Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 2019

The Hierarchical Continuous Pursuit Learning Automation: A Novel Scheme for Environments With Large Numbers of Actions – IEEE Transactions on Neural Networks and Learning Systems 2019

A Scheme for Continuous Input to the Tsetlin Machine with Applications to Forecasting Disease Outbreaks – 2019

A Conclusive Analysis of the Finite-Time Behavior of the Discretized Pursuit Learning Automaton – IEEE Transactions on Neural Networks and Learning Systems 2019

The Hierarchical Continuous Pursuit Learning Automation for Large Numbers of Actions – 2018

The design of absorbing Bayesian pursuit algorithms and the formal analyses of their ε-optimality – Pattern Analysis and Applications 2016

Optimizing channel selection for cognitive radio networks using a distributed Bayesian learning automata-based approach – Applied intelligence (Boston) 2016

A formal proof of the e-optimality of discretized pursuit algorithms – Applied intelligence (Boston) 2015

Using the Theory of Regular Functions to Formally Prove the ε -Optimality of Discretized Pursuit Learning Algorithms – 2014

A Bayesian Learning Automata-Based Distributed Channel Selection Scheme – 2014

A formal proof of the ε-optimality of absorbing continuous pursuit algorithms using the theory of regular functions – Applied intelligence (Boston) 2014

On Using the Theory of Regular Functions to Prove the Epsilon-Optimality of the Continuous Pursuit Learn- ing Automaton – 2013

Channel selection in cognitive radio networks: A switchable Bayesian learning automata approach – 2013

On incorporating the paradigms of discretization and Bayesian estimation to create a new family of pursuit learning automata – Applied intelligence (Boston) 2013

Discretized Bayesian Pursuit – A New Scheme for Reinforcement Learning – 2012

The Bayesian Pursuit Algorithm: A New Family of Estimator Learning Automata – 2011

Generalized Bayesian Pursuit: A Novel Scheme for Multi-Armed Bernoulli Bandit Problems – 2011

Generalized Bayesian pursuit: a novel scheme for multi-armed Bernoulli bandit problems – IFIP Advances in Information and Communication Technology 2011

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