A Two-Stage Conditional Random Field Model Based Framework for Multi-Label Classification

DOI10.1007/978-3-319-69900-4_9zbMath1498.68265OpenAlexW2765126435MaRDI QIDQ5045355

C. K. Chandrasekhar, Abhiram Kumar Singh

Publication date: 4 November 2022

Published in: Lecture Notes in Computer Science (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/978-3-319-69900-4_9

zbMATH Keywords

multi-label classificationconditional random fieldlabel dependence

Mathematics Subject Classification ID

Random fields; image analysis (62M40) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05)

Uses Software

• L-BFGS
• MULAN
• ML-KNN
• PMTK
• UGM

Cites Work

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• ML-KNN: A lazy learning approach to multi-label learning
• A scalable pairwise class interaction framework for multidimensional classification
• On the limited memory BFGS method for large scale optimization
• Efficiency of pseudolikelihood estimation for simple Gaussian fields
• Model Selection and Estimation in Regression with Grouped Variables

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