Collaborative Data Acquisition and Learning Support - Publication - Bridge of Knowledge

Your account

login

Search

Collaborative Data Acquisition and Learning Support

Abstract

With the constant development of neural networks, traditional algorithms relying on data structures lose their significance as more and more solutions are using AI rather than traditional algorithms. This in turn requires a lot of correctly annotated and informative data samples. In this paper, we propose a crowdsourcing based approach for data acquisition and tagging with support for Active Learning where the system acts as an oracle and repository of training samples. The paper presents the CenHive system implementing the proposed approach. Three different usage scenarios are presented that were used to verify the proposed approach.

Citations

  • 1

    CrossRef

  • 0

    Web of Science

  • 1

    Scopus

Authors (2)

Cite as

Full text

download paper
downloaded 58 times
Publication version
Accepted or Published Version
License
Copyright (Springer Nature Switzerland AG 2020)

Keywords

Details

Category:
Monographic publication
Type:
rozdział, artykuł w książce - dziele zbiorowym /podręczniku w języku o zasięgu międzynarodowym
Published in:
International Journal of Computer Information Systems and Industrial Management Applications pages 220 - 229,
ISSN:
Title of issue:
Computer Information Systems and Industrial Management strony 220 - 229
Language:
English
Publication year:
2020
Bibliographic description:
Boiński T., Szymański J.: Collaborative Data Acquisition and Learning Support// Computer Information Systems and Industrial Management/ : , 2020, s.220-229
DOI:
Digital Object Identifier (open in new tab) 10.1007/978-3-030-47679-3_19
Verified by:
Gdańsk University of Technology

Referenced datasets

seen 180 times

Recommended for you

Active Annotation in Evaluating the Credibility of Web-Based Medical Information: Guidelines for Creating Training Data Sets for Machine Learning

2021

Medical Image Dataset Annotation Service (MIDAS)

2020

Active Learning Based on Crowdsourced Data

2022

Quantifying inconsistencies in the Hamburg Sign Language Notation System

  • M. Ferlin,
  • S. Majchrowska,
  • M. A. Plantykow
  • + 4 authors
2024
Meta Tags