4.7 Article

Biography and future challenges

期刊

MACHINE LEARNING
卷 86, 期 1, 页码 3-23

出版社

SPRINGER
DOI: 10.1007/s10994-011-5259-2

关键词

Inductive Logic Programming; (Statistical) relational learning; Structured data in Machine Learning

资金

  1. Royal Academy of Engineering and Microsoft Research
  2. Syngenta Ltd in the University Innovation Centre at Imperial College
  3. BBSRC
  4. Centre for Integrative Systems Biology at Imperial College
  5. Grants-in-Aid for Scientific Research [20240016] Funding Source: KAKEN

向作者/读者索取更多资源

Inductive Logic Programming (ILP) is an area of Machine Learning which has now reached its twentieth year. Using the analogy of a human biography this paper recalls the development of the subject from its infancy through childhood and teenage years. We show how in each phase ILP has been characterised by an attempt to extend theory and implementations in tandem with the development of novel and challenging real-world applications. Lastly, by projection we suggest directions for research which will help the subject coming of age.

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