Logical and relational learning

Autor*innen

• Luc De Raedt

Parameter

• 325 Seiten
• 12 Lesestunden

• Informatik & Programmierung

Mehr zum Buch

The terms logical and relational learning refer to the subfield of artificial intelligence, machine learning, and data mining focused on learning in expressive logical or relational representations. This area combines inductive logic programming, statistical relational learning, and multi-relational data mining, all of which provide techniques for learning from relational data. Although early contributions date back about forty years, the field gained popularity with the rise of inductive logic programming in the early 1990s. Initial efforts primarily addressed logical issues, but the focus quickly shifted to discovering new and interpretable knowledge from structured data, often represented as rules. Significant successes in applications emerged in domains such as bioinformatics, cheminformatics, and computational linguistics. Today, the artificial intelligence community actively engages with the challenges and opportunities of structured data, driving ongoing research. Graph, network, and multi-relational data mining have become prominent themes, while statistical relational learning is increasingly recognized within machine learning and uncertainty in AI. Furthermore, logical and relational techniques now encompass nearly all tasks in machine learning and data mining.

• Sprache: Englisch
• Erscheinungsdatum: 2008
• Einband: Hardcover