Exploiting label dependencies for improved sample complexity

DOI10.1007/s10994-012-5312-9zbMath1273.68294OpenAlexW2034845197MaRDI QIDQ374170

Lior Rokach, Dan Gutfreund, Lena Chekina, Bracha Shapira, Leonid (Aryeh) Kontorovich

Publication date: 22 October 2013

Published in: Machine Learning (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10994-012-5312-9

zbMATH Keywords

multi-label classificationartificial datasetsconditional and unconditional label dependenceempirical experimentensemble learning algorithmsensemble models diversitygeneralization boundsmulti-label evaluation measures

Mathematics Subject Classification ID

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

Related Items (1)

A stacking weighted \(k\)-nearest neighbour with thresholding

Uses Software

• MULAN
• BoosTexter

Cites Work

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• Toward efficient agnostic learning
• BoosTexter: A boosting-based system for text categorization
• Genetic algorithm-based feature set partitioning for classification problems
• Learnability and the Vapnik-Chervonenkis dimension
• On the Uniform Convergence of Relative Frequencies of Events to Their Probabilities
• Convergence of stochastic processes

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