Boosting over non-deterministic ZDDs

DOI10.1016/j.tcs.2018.11.027zbMath1436.68301OpenAlexW4210482011WikidataQ128827329 ScholiaQ128827329MaRDI QIDQ5919322

Eiji Takimoto, Kohei Hatano, Takahiro Fujita

Publication date: 16 January 2020

Published in: Theoretical Computer Science (Search for Journal in Brave)

Full work available at URL: http://hdl.handle.net/2324/2231502

zbMATH Keywords

optimizationdata compressionmachine learningboostingZDD

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Coding and information theory (compaction, compression, models of communication, encoding schemes, etc.) (aspects in computer science) (68P30) Data structures (68P05)

Uses Software

• AdaBoost.MH
• LCM

Cites Work

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• Minimisation of acyclic deterministic automata in linear time
• Arbitrary-norm separating plane
• A decision-theoretic generalization of on-line learning and an application to boosting
• Fast \(q\)-gram mining on SLP compressed strings
• Processing Compressed Texts: A Tractability Border
• Minimal NFA Problems are Hard
• 10.1162/1532443041424328

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