Please use this identifier to cite or link to this item: https://open.uns.ac.rs/handle/123456789/3300
Title: RSSalg software: A tool for flexible experimenting with co-training based semi-supervised algorithms
Authors: Slivka, Jelena 
Sladić, Goran 
Milosavljević, Branko 
Kovačević, Aleksandar 
Issue Date: 1-Apr-2017
Journal: Knowledge-Based Systems
Abstract: © 2017 RSSalg software is a tool for experimenting with Semi-Supervised Learning (SSL), a set of machine learning techniques able to use both labeled and unlabeled data for training. The goal is to reduce human effort regarding data labeling while preserving model quality. RSSalg software encompasses the implementation of co-training, a multi-view SSL technique and RSSalg, its single-view alternative. Our tool enables easy comparison of different SSL algorithms. It provides a cross-validation procedure and supports standard metrics for performance evaluation. The tool is free and open source, available on GitHub under the GNU General Public License. It is implemented in Java language using Weka library.
URI: https://open.uns.ac.rs/handle/123456789/3300
ISSN: 09507051
DOI: 10.1016/j.knosys.2017.01.024
Appears in Collections:Naučne i umetničke publikacije

Files in This Item:
File SizeFormat
rssalg.pdf264.29 kBAdobe PDFView/Open
Show full item record

SCOPUSTM   
Citations

5
checked on Jul 1, 2020

Page view(s)

253
checked on Jul 11, 2020

Download(s)

54
checked on Jul 11, 2020

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.