.png){kind=icon}
Return
INDUCTIVE LEARNING IN DEDUCTIVE DATABASES
.png){kind=icon}
PRE
AI
DOI：10.1109/69.250076.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Most current applications of inductive learning in databases take place in the context of a single extensional relation. This paper puts inductive learning in the context of a set of relations defined either extensionally or intentionally in the framework of deductive databases. It presents LINUS, an inductive logic programming system that induces virtual relations from example positive and negative tuples and already defined relations in a deductive database. Based on the idea of transforming the problem of learning relations to attribute-value form, it incorporates several attribute-value learning systems. As the latter handle noisy data successfully, LINUS is able to learn relations from real life noisy databases. The paper illustrates the use of LINUS for learning virtual relations and then presents a study of its performance on noisy data.
Keywords:
DEDUCTIVE DATABASES
INDUCTIVE LOGIC PROGRAMMING
MACHINE LEARNING
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
10.4
Papers:
6.8K
Citations:
3.2W
Organization
No organization information available
Cited Papers
.png){kind=icon}
ShareSaveShareSaveShareSaveShareSave
View more