## Relating Relational Learning Algorithms (1992)

Venue: | Inductive Logic Programming |

Citations: | 7 - 0 self |

### BibTeX

@INPROCEEDINGS{Aha92relatingrelational,

author = {David Aha},

title = {Relating Relational Learning Algorithms},

booktitle = {Inductive Logic Programming},

year = {1992},

pages = {233--260},

publisher = {Academic Press}

}

### OpenURL

### Abstract

Relational learning algorithms are of special interest to members of the machine learning community; they offer practical methods for extending the representations used in algorithms that solve supervised learning tasks. Five approaches are currently being explored to address issues involved with using relational representations. This paper surveys algorithms embodying these approaches, summarizes their empirical evaluations, highlights their commonalities, and suggests potential directions for future research. Keywords: supervised learning, representation, relational learning 1 Introduction Relational learning algorithms extend the capabilities of propositional or monadic supervised learning algorithms. Supervised learning algorithms input a set of instances, which are described by a set of predictor descriptors and a target descriptor. These algorithms construct a function (i.e., a concept description) that can predict an instance's target descriptor value given its predictor desc...