.png){kind=icon}
返回
General solution and learning method for binary classification with performance constraints
.png){kind=icon}
PRE
AI
DOI：10.1016/j.patrec.2008.02.025.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In this paper, the problem of binary classification is studied with one or two performance constraints. When the constraints cannot be satisfied, the initial problem has no solution and an alternative problem is solved by introducing a rejection option. The optimal solution for such problems in the framework of statistical hypothesis testing is shown to be based on likelihood ratio with one or two thresholds depending on whether it is necessary to introduce a rejection option or not. These problems are then addressed when classes are only defined by labelled samples. To illustrate the resolution of cases with and without rejection option, the problem of Neyman-Pearson and the one of minimizing reject probability subject to a constraint on error probability are studied. Solutions based on SVMs and on a kernel based classifier are experimentally compared and discussed. (C) 2008 Elsevier B.V. All rights reserved.
Keyword:
statistical hypothesis testing
performance constraints
Neyman-Pearson criterion
Chow's rule
classification with rejection option
kernel methods
期刊
IF:
3.3
论文数:
7.9K
被引数:
1.6W
机构
引用论文
.png){kind=icon}
Dichroic extinction and the infrared polarization of young stellar objects in the L1641 dark cloud
OAAI
OAAI分享收藏Superstitious seasonality in precious metals markets? Evidence from GARCH models with time-varying skewness and kurtosis贵金属市场的迷信季节性？具有时变偏度和峰度的GARCH模型的证据
PREAI
分享收藏分享收藏Dysfunctions of decision‐making and cognitive control as transdiagnostic mechanisms of mental disorders: advances, gaps, and needs in current research作为精神障碍的跨诊断机制的决策和认知控制功能障碍: 当前研究的进展，差距和需求OAAI
OAAI分享收藏
查看更多内容