.png){kind=icon}
Return
Learning concept descriptions with typed evolutionary programming
.png){kind=icon}
PRE
AI
DOI：10.1109/TKDE.2005.199.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Examples and concepts in traditional concept learning tasks are represented with the attribute-value language. While enabling efficient implementations, we argue that such propositional representation is inadequate when data is rich in structure. This paper describes STEPS, a strongly-typed evolutionary programming system designed to induce concepts from structured data. STEPS' higher-order logic representation language enhances expressiveness, while the use of evolutionary computation dampens the effects of the corresponding explosion of the search space. Results on the PTE2 challenge, a major real-world knowledge discovery application from the molecular biology domain, demonstrate promise.
Keywords:
concept learning
typed evolutionary programming
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
ShareSavePlant population size and isolation affect herbivory of Silene latifolia by the specialist herbivore Hadena bicruris and parasitism of the herbivore by parasitoids.png)
Oecologia
OecologiaIF0
PREAI
ShareSave
ShareSaveShareSaveShareSaveShareSave no more
no more