End-user feature labeling: supervised and semi-supervised approaches based on locally-weighted logistic regression

DOI10.1016/j.artint.2013.08.003zbMath1334.68181OpenAlexW1987391560MaRDI QIDQ490436

Ian Oberst, Kevin McIntosh, Margaret Burnett, Simone Stumpf, Weng-Keen Wong, Shubhomoy Das, Travis Moore

Publication date: 27 August 2015

Published in: Artificial Intelligence (Search for Journal in Brave)

Full work available at URL: http://openaccess.city.ac.uk/2741/1/AIJ13-dassh.pdf

zbMATH Keywords

machine learningsemi-supervised learningfeature labelingintelligent interfaceslocally-weighted logistic regression

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05)

Related Items (1)

Learning with rationales for document classification

Uses Software

• L-BFGS
• MALLET
• ConceptNet

Cites Work

• Locally Weighted Regression: An Approach to Regression Analysis by Local Fitting
• Introduction to Semi-Supervised Learning
• Updating Quasi-Newton Matrices with Limited Storage
• Unnamed Item
• Unnamed Item
• Unnamed Item

This page was built for publication: End-user feature labeling: supervised and semi-supervised approaches based on locally-weighted logistic regression