Tree edit distance for hierarchical data compatible with HMIL paradigm

Autor:	Šopík, Břetislav, Strenáčik, Tomáš
Rok vydání:	2022
Předmět:	Computer Science - Databases Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	We define edit distance for hierarchically structured data compatible with the hierarchical multi-instance learning paradigm. Example of such data is dataset represented in JSON format where inner Array objects are interpreted as unordered bags of elements. We prove correct analytical properties of the defined distance.
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2208.00782 Zobrazit plný text záznamu View this record from Arxiv