AI RESEARCH
High-Dimensional Analysis of Bootstrap Ensemble Classifiers
arXiv CS.LG
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ArXi:2505.14587v2 Announce Type: replace-cross Bootstrap methods have long been the cornerstone of ensemble learning in machine learning. This paper presents a theoretical analysis of bootstrap techniques applied to the Least Square Vector Machine (LSSVM) ensemble in the context of large and growing sample sizes and feature dimensionalities. Using tools from Random Matrix Theory, we investigate the performance of this classifier that aggregates decision functions from multiple weak classifiers, each trained on different subsets of the data.