Optimal Linear Combination of Classifiers
arXiv:2103.01109v1
Originality Synthesis-oriented
AI Analysis
This addresses the fundamental decision in machine learning for practitioners, but appears incremental as it builds on existing bias-variance concepts.
The paper tackles the problem of whether to use a single classifier or a combination, proposing a method to find an optimal linear combination based on a bias-variance framework for classification.
The question of whether to use one classifier or a combination of classifiers is a central topic in Machine Learning. We propose here a method for finding an optimal linear combination of classifiers derived from a bias-variance framework for the classification task.