学习样本存在分类错误时的判据稳定性问题

On Stability of Decision Rules in the Presence of Errors in Classification of the Learning Sample

讨论学习样本的分类可靠性难以确信时的模式识别问题 ,研究利用条件密度极大似然估计进行贝叶斯风险决策时 ,学习样本分类错误对它的影响 ,以及在此情况下的判据稳定性问题。

Pattern recognition problems are considered for the case of inaccessible credible classification of the learning sample. The effect of errors in classification of the learning sample on the risk of the Bayes decision rule is studied in the case of estimating the maximal likelihood of the conditional density parameters. Analytically and numerically solvable decision rules stable with errors in the learning sample are proposed and studied.