Learning Tversky Similarity

Rahnama, Javad und Hüllermeier, Eyke (5. Juni 2020): Learning Tversky Similarity. International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, Lisbon, Portugal, 15-19 June 2020. In: Information Processing and Management of Uncertainty in Knowledge-Based Systems, Bd. 1238 Cham: Springer. S. 269-280

Abstract

In this paper, we advocate Tversky’s ratio model as an appropriate basis for computational approaches to semantic similarity, that is, the comparison of objects such as images in a semantically meaningful way. We consider the problem of learning Tversky similarity measures from suitable training data indicating whether two objects tend to be similar or dissimilar. Experimentally, we evaluate our approach to similarity learning on two image datasets, showing that is performs very well compared to existing methods.

• Dokumententyp: Konferenzbeitrag (Paper)
• Publikationsform: Publisher's Version
• Fakultät: Mathematik, Informatik und Statistik > Informatik > Künstliche Intelligenz und Maschinelles Lernen
• Themengebiete: 000 Informatik, Informationswissenschaft, allgemeine Werke > 000 Informatik, Wissen, Systeme
• ISSN: 1865-0929
• Ort: Cham
• Sprache: Englisch