Conditional Linear Regression

被引：0

作者：

Calderon, Diego ^{[1
]}

Juba, Brendan ^{[2
]}

Li, Zongyi ^{[2
]}

Ruan, Lisa ^{[3
]}

机构：

[1] Univ Arkansas, Fayetteville, AR 72701 USA

[2] Washington Univ, St Louis, MO 63110 USA

[3] MIT, Cambridge, MA 02139 USA

来源：

THIRTY-SECOND AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE / THIRTIETH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE / EIGHTH AAAI SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE | 2018年

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Previous work in machine learning and statistics commonly focuses on building models that capture the vast majority of data, possibly ignoring a segment of the population as outliers. By contrast, we may be interested in finding a segment of the population for which we can find a linear rule capable of achieving more accurate predictions. We give an efficient algorithm for the conditional linear regression task, which is the joint task of identifying a significant segment of the population, described by a k-DNF, along with its linear regression fit.

引用

页码：8055 / 8056

页数：2

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