Treffer: Software package for regression algorithms based on Gaussian Conditional Random Fields

Title:
Software package for regression algorithms based on Gaussian Conditional Random Fields
Authors:
Markovic, Tijana, Devedzic, V., Zhou, F., Obradovic, Z.
Publisher Information:
Mälardalens universitet, Inbyggda system University of Belgrade, Faculty of Organizational Sciences, Belgrade, Serbia East China Normal University, School of Data Science and Engineering, Shanghai, China Temple University, Center for Data Analytics and Biomedical Informatics, Philadelphia, United States Institute of Electrical and Electronics Engineers Inc. 2022
Document Type:
E-Ressource Electronic Resource
Index Terms:
Gaussian Conditional Random Fields, Graphs, Library, Regression Algorithms, Software, Gaussian distribution, Image segmentation, Java programming language, Machine learning, Open source software, Random processes, Regression analysis, Software packages, Software testing, Different domains, Gaussian conditional random field, Gaussians, Graph, Java library, Machine-learning, Random fields, Regression problem, Graphical user interfaces, Computer Sciences, Datavetenskap (datalogi), Conference paper, info:eu-repo/semantics/conferenceObject, text
DOI:
10.1109.ICMLA55696.2022.00184
URL:
http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-62282
Availability:
Open access content. Open access content
info:eu-repo/semantics/restrictedAccess
Note:
English
Other Numbers:
UPE oai:DiVA.org:mdh-62282
urn:isbn:9781665462839
doi:10.1109/ICMLA55696.2022.00184
ISI:000980994900173
Scopus 2-s2.0-85152213930
1387555485
Contributing Source:
UPPSALA UNIV LIBR
From OAIster®, provided by the OCLC Cooperative.
Accession Number:
edsoai.on1387555485
Database:
OAIster

Weitere Informationen

The Gaussian Conditional Random Fields (GCRF) algorithm and its extensions are used for machine learning regression problems in which the attributes of objects and the correlation between objects should be considered when making predictions. These algorithms can be applied in different domains where problems can be seen as graphs, but their implementation requires complex calculations and good programming skills. This paper presents an open source software package that includes a tool with graphical user interface (GCRFs tool) and Java library (GCRFs library). GCRFs tool is software that integrates various GCRF-based algorithms and supports training and testing of those algorithms on real-world datasets. The main goal of GCRFs tool is to provide a straightforward and user-friendly graphical user interface that will simplify the use of GCRF-based algorithms. GCRFs Java library contains basic classes for GCRF concepts and can be used by researchers who have experience in Java programming. Also, this paper presents the results of a pilot usability evaluation of the GCRFs tool, where the software was evaluated with expert and non-expert users. This evaluation gave us detailed insight into the experiences and opinions of the users and helped us outline priorities for future development.