@inproceedings{5e1f2b3005334b0cb4d2d329254cc3bb,
title = "Software package for regression algorithms based on Gaussian Conditional Random Fields",
abstract = "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.",
keywords = "Gaussian Conditional Random Fields, Graphs, Library, Regression Algorithms, Software",
author = "Tijana Markovic and Vladan Devedzic and Fang Zhou and Zoran Obradovic",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 21st IEEE International Conference on Machine Learning and Applications, ICMLA 2022 ; Conference date: 12-12-2022 Through 14-12-2022",
year = "2022",
doi = "10.1109/ICMLA55696.2022.00184",
language = "英语",
series = "Proceedings - 21st IEEE International Conference on Machine Learning and Applications, ICMLA 2022",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1121--1128",
editor = "Wani, \{M. Arif\} and Mehmed Kantardzic and Vasile Palade and Daniel Neagu and Longzhi Yang and Kit-Yan Chan",
booktitle = "Proceedings - 21st IEEE International Conference on Machine Learning and Applications, ICMLA 2022",
address = "美国",
}