Learning Performance of Nonlinear Classification Models Based on Markov Sampling
Nonlinear classification models excel in handling complex problems and are widely used across various fields. This paper explores the learning performance of nonlinear classification models using Markov sampling, an extension of the traditional i.i.d. sample framework. It introduces a ueMC-NL algorithm specifically designed for these models to facilitate the generation of ueMC samples from a finite dataset. Numerical analyses of the random forest and MLP model show that nonlinear classification models utilizing ueMC samples achieve lower misclassification rates compared to those using i.i.d. samples.
Jan 1, 0001