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Oct, 2022
利用调节正则化的极大似然估计改进多类别分类器
Improving Multi-class Classifier Using Likelihood Ratio Estimation with Regularization
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Masato Kikuchi, Tadachika Ozono
TL;DR
本文融合了前人的研究成果，提出了一种新的分类器，使用正则化参数有效地调整分类得分，并在不平衡数据的情况下提高了准确性。
Abstract
The
universal-set naive bayes classifier
(UNB)~\cite{Komiya:13}, defined using
likelihood ratios
(LRs), was proposed to address imbalanced classification problems. However, the LR estimator used in the UNB overes
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