Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/154134
Title: Semi-supervised RVFL-based neural networks for solving classification problems
Authors: Yao, Cheng Hui
Keywords: Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Issue Date: 2021
Publisher: Nanyang Technological University
Source: Yao, C. H. (2021). Semi-supervised RVFL-based neural networks for solving classification problems. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/154134
Project: A1101-211
Abstract: Random Vector Functional Link (RVFL) is widely used on supervised tasks. However, in the real world, we often have a small number of labelled samples and many unlabelled samples. In this paper, we extend RVFLs for semi-supervised tasks based on Manifold Regularization (MR), thus expanding on the application of RVFL to semi-supervised tasks. MR has been deeply researched in the past decade to improve the quality of classifiers making use of unlabelled data. Following this MR approach, semi-supervised RVFL (SS-RVFL) demonstrates great improvements in performance in comparison to typical RVFL networks. The method enhances RVFL based classifiers for semi-supervised learning while still retaining the efficiency of RVFL networks. In these experiments, we are also proposing the use of Deep RVFL networks for semisupervised learning. There have not been as much research regarding the use of semisupervised deep RVFL networks. Hence we will be applying the MR approach to Deep RVFL (dRVFL) and Ensemble Deep RVFL (edRVFL) for semi-supervised classification problems as well. Deep variants of the RVFL network are able to gain information from various enhanced patterns which could help in improving the performance of a learning algorithm. We present an evaluation on well-known datasets to demonstrate the performance of the proposed methods.
URI: https://hdl.handle.net/10356/154134
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)

Files in This Item:
File Description SizeFormat 
Semi-Supervised RVFL-Based Neural Networks for Solving Classification Problems.pdf
  Restricted Access
580.37 kBAdobe PDFView/Open

Page view(s)

12
Updated on Jan 21, 2022

Download(s)

2
Updated on Jan 21, 2022

Google ScholarTM

Check

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.