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Efficient Learning of Horn Formulas over Finite Totally Ordered Domains

  • 2026
  • OriginalPaper
  • Chapter
Published in:
Authors
Miki Hermann
Gernot Salzer
Published in
Multi-disciplinary Trends in Artificial Intelligence
Publisher
Springer Nature Singapore

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Abstract

This chapter explores the efficient learning of Horn formulas over finite totally ordered domains, a critical area in machine learning. The text delves into the concept of PAC-learning and the significance of Horn formulas, which can be understood as if-then rules. It discusses the challenges of binarization and introduces signed logic as a solution to avoid these issues. The chapter presents polynomial-time algorithms for both offline and online settings, ensuring that the learned formulas are both efficient and interpretable. The experiments reported demonstrate the effectiveness of this approach, making it a valuable resource for professionals in the field.
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previous chapter Uncertainty Quantification for Flood Forecasting in Small Catchments
next chapter Knowledge-Enhanced Vietnamese Paraphrase Identification
Title
Efficient Learning of Horn Formulas over Finite Totally Ordered Domains
Authors
Miki Hermann
Gernot Salzer
Copyright Year
2026
Publisher
Springer Nature Singapore
Book
Multi-disciplinary Trends in Artificial Intelligence

Print ISBN: 978-981-9549-59-7

Electronic ISBN: 978-981-9549-60-3

Copyright Year: 2026

DOI
https://doi.org/10.1007/978-981-95-4960-3_7
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