6533b862fe1ef96bd12c7492
RESEARCH PRODUCT
A Brief History of Learning Symbolic Higher-Level Representations from Data (And a Curious Look Forward)
Stefan Kramersubject
Computer sciencedescription
Learning higher-level representations from data has been on the agenda of AI research for several decades. In the paper, I will give a survey of various approaches to learning symbolic higher-level representations: feature construction and constructive induction, predicate invention, propositionalization, pattern mining, and mining time series patterns. Finally, I will give an outlook on how approaches to learning higher-level representations, symbolic and neural, can benefit from each other to solve current issues in machine learning.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2020-07-01 | Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence |