Saved in:
Bibliographic Details
Main Authors: Levy, Avivit, Shalom, B. Riva, Chalamish, Michal
Format: Preprint
Published: 2024
Subjects:
Online Access:https://arxiv.org/abs/2408.07706
Tags: Add Tag
No Tags, Be the first to tag this record!
_version_ 1866916358252396544
author Levy, Avivit
Shalom, B. Riva
Chalamish, Michal
author_facet Levy, Avivit
Shalom, B. Riva
Chalamish, Michal
contents Similarity measures play a central role in various data science application domains for a wide assortment of tasks. This guide describes a comprehensive set of prevalent similarity measures to serve both non-experts and professional. Non-experts that wish to understand the motivation for a measure as well as how to use it may find a friendly and detailed exposition of the formulas of the measures, whereas experts may find a glance to the principles of designing similarity measures and ideas for a better way to measure similarity for their desired task in a given application domain.
format Preprint
id arxiv_https___arxiv_org_abs_2408_07706
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle A Guide to Similarity Measures
Levy, Avivit
Shalom, B. Riva
Chalamish, Michal
Information Retrieval
Computer Vision and Pattern Recognition
Machine Learning
Similarity measures play a central role in various data science application domains for a wide assortment of tasks. This guide describes a comprehensive set of prevalent similarity measures to serve both non-experts and professional. Non-experts that wish to understand the motivation for a measure as well as how to use it may find a friendly and detailed exposition of the formulas of the measures, whereas experts may find a glance to the principles of designing similarity measures and ideas for a better way to measure similarity for their desired task in a given application domain.
title A Guide to Similarity Measures
topic Information Retrieval
Computer Vision and Pattern Recognition
Machine Learning
url https://arxiv.org/abs/2408.07706