JSM 2012 Home

JSM 2012 Online Program

The views expressed here are those of the individual authors and not necessarily those of the JSM sponsors, their officers, or their staff.

Online Program Home

Abstract Details

Activity Number: 295
Type: Contributed
Date/Time: Tuesday, July 31, 2012 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistical Learning and Data Mining
Abstract - #304660
Title: Weighted Logistic Regression Modeling for Semi-Supervised Classification
Author(s): Shuichi Kawano*+
Companies: Osaka Prefecture University
Address: 1-1 Gakuen-Cho, Osaka, 599-8531, Japan
Keywords: Covariate shift ; Model selection ; Regularization ; Semi-supervised learning

Semi-supervised learning, which is a statistical method that combines both labeled and unlabeled data, has received considerable attention in contemporary statistics and machine learning. There are a lot of modeling approaches for semi-supervised learning; e.g., a mixture modeling approach, a graph theory approach, a boosting approach, a logistic regression modeling approach and so on. Most of these semi-supervised approaches implicitly assume that a density function for labeled data is the same as that for unlabeled data. In this talk, we propose a weighted logistic model for semi-supervised classification problem by using covariate shift adaptation techniques in the situation that the density function for labeled data is different from that for unlabeled data. A crucial issue in modeling process is the choice of adjusted parameters included in the models. In order to select the adjusted parameters, we introduce an information criterion that evaluates our semi-supervised logistic models. Numerical examples are given to examine the effectiveness of proposed semi-supervised discrimination.

The address information is for the authors that have a + after their name.
Authors who are presenting talks have a * after their name.

Back to the full JSM 2012 program

2012 JSM Online Program Home

For information, contact jsm@amstat.org or phone (888) 231-3473.

If you have questions about the Continuing Education program, please contact the Education Department.