Semi-supervised logistic discrimination via labeled data and unlabeled data from different sampling distributions

DOI10.1002/sam.11204zbMath1281.62148arXiv1108.5244OpenAlexW2949866066MaRDI QIDQ2870752

Shuichi Kawano

Publication date: 21 January 2014

Published in: Statistical Analysis and Data Mining: The ASA Data Science Journal (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1108.5244

zbMATH Keywords

regularizationEM algorithmmodel selectionsemi-supervised learningcovariate shifts

Mathematics Subject Classification ID

Related Items (1)

Classification from only positive and unlabeled functional data

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Direct importance estimation for covariate shift adaptation
• Semi-supervised learning with density-ratio estimation
• The use of unlabeled data in predictive modeling
• Bayesian semi-supervised learning with support vector machine
• Nonlinear regression modeling via regularized radial basis function networks
• Improving predictive inference under covariate shift by weighting the log-likelihood function
• Information criteria and statistical modeling.
• Density Ratio Estimation in Machine Learning
• Semi-Supervised Logistic Discrimination via Regularized Gaussian Basis Expansions
• Generalised information criteria in model selection
• Information and Posterior Probability Criteria for Model Selection in Local Likelihood Estimation
• Using Unlabelled Data to Update Classification Rules with Applications in Food Authenticity Studies

This page was built for publication: Semi-supervised logistic discrimination via labeled data and unlabeled data from different sampling distributions