Professor Shulan Hu and Associate Professor Xinyu Wang, the researchers of the base, has published a collaborative paper titled "Learning Performance of the Perceptron Model Based on Markov Sampling" in Acta Mathematicae Applicatae Sinica.

Abstract: The perceptron model is an essential algorithm in machine learning andartificial intelligence, functioning as a linear classifier for binary classification tasks.This paper investigates the learning performance of the perceptron model based onMarkov sampling, which builds upon the traditional framework with independent andidentically distributed samples. Initial efforts establish the constraints on the percep-tron model’s learning performance with uniformly ergodic Markov chain samples andvalidates its consistent behavior. Furthermore, the paper introduces a ueMC-PM algo-rithm. Numerical investigations undertaken on benchmark repositories reveal that theperceptron model utilizing ueMC samples yields fewer misclassification rates.

Keywords: learning performance; Markov sampling;perceptron model; uniformly ergodic Markov chain

Link: 基于马尔可夫抽样的感知机模型的学习性能分析

Teacher profile

Shulan Hu, Professor and Ph.D. Supervisor, Zhongnan University of Economics and Law; Director of the Research Center for Digital Intelligence Development; Researcher at the Base for Introducing Talents in Digital Technology and Modern Finance Discipline Innovation; First Batch of Wenlan Young Scholars; Director of the Resource and Environment Branch of the Chinese Association for Applied Statistics; Director of the Big Data Branch; Director of the Hubei Provincial Association for Applied Statistics; Member of the Hubei Provincial Technical Committee for Data Standardization. "Double-Qualified" Tutor of the Second Batch of Industry-Education Integration Training Bases of CCB College. She once served as Deputy General Manager of the Financial Technology Department and Financial Accounting Department (Digitalization Office) of China Construction Bank Hubei Branch on secondment.Research Interests: Theory of Stochastic Algorithms for Big Data and its Applications; Financial Risk Modeling, Econometrics; Development and Utilization of Data Circulation, Data Asset/Product Evaluation Models, Data Standards.She has presided over and completed multiple projects including the National Natural Science Foundation of China and the National Social Science Foundation of China. She has also presided over more than 20 projects such as those of Central Universities, Graduate Case Bank Projects, Quality Course Projects, All-English Course Construction Projects, MBA Case Bank Projects, and Enterprise Horizontal Projects. She has published more than 30 papers in renowned domestic and foreign journals, published 1 national-level "14th Five-Year Plan" textbook and 1 monograph, guided students to win more than 30 national-level awards in various competitions, and participated in the formulation of multiple data resource-related standards.

Xinyu Wang, Associate Professor and Master's Supervisor, Zhongnan University of Economics and Law.Research Interests: Big Data Statistical Analysis; Theory of Stochastic Algorithms; Applications of Stochastic Processes and Stochastic Analysis in Economics and Finance.He has published more than 20 papers in authoritative domestic and foreign journals such as Bernoulli, Mathematics of Computation, Discrete and Continuous Dynamical Systems, International Journal for Uncertainty Quantification, Stochastic and Dynamics, Acta Applicandae Mathematicae, Acta Mathematica Scientia, Statistics & Probability Letters, and Acta Mathematica Sinica.