A utility-driven approach to instance-based transfer learning for relational domains

Statistical relational learning involves exploring a complex search space of objects, their relationships, and probability parameters to find an optimal model. To reduce search complexity, previous work has explored taking advantage of a learned model in a source domain and transfer it to a target d...

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Veröffentlicht in:	Machine learning Jg. 114; H. 11; S. 261
Hauptverfasser:	Pereira, Cainã F., Menasché, Daniel S., Zaverucha, Gerson, Paes, Aline, Barbosa, Valmir C.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.11.2025 Springer Nature B.V
Schlagworte:	Artificial Intelligence Complexity Computer networks Computer Science Control Learning Machine Learning Mechatronics Natural Language Processing (NLP) Resource allocation Robotics Simulation and Modeling Statistical analysis Utilities Utility functions Relational learning Utility maximization Statistical relational learning Transfer learning
ISSN:	0885-6125, 1573-0565
Online-Zugang:	Volltext
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