Please use this identifier to cite or link to this item: http://ktisis.cut.ac.cy/handle/10488/7051
Title: Neural network methods for one-to-many multi-valued mapping problems
Authors: Jayne, Chrisina
Christodoulou, Chris
Lanitis, Andreas 
Keywords: Neural computing;Neural networks (Computer science)
Category: Arts
Field: Humanities
Issue Date: 2011
Publisher: Springer-Verlag
Source: Neural computing and applications, 2011, Volume 20, Issue 6 , pp 775-785
Abstract: An investigation of the applicability of neural network-based methods in predicting the values of multiple parameters, given the value of a single parameter within a particular problem domain is presented. In this context, the input parameter may be an important source of variation that is related with a complex mapping function to the remaining sources of variation within a multivariate distribution. The definition of the relationship between the variables of a multivariate distribution and a single source of variation allows the estimation of the values of multiple variables given the value of the single variable, addressing in that way an ill-conditioned one-to-many mapping problem. As part of our investigation, two problem domains are considered: predicting the values of individual stock shares, given the value of the general index, and predicting the grades received by high school pupils, given the grade for a single course or the average grade. With our work, the performance of standard neural network-based methods and in particular multilayer perceptrons (MLPs), radial basis functions (RBFs), mixture density networks (MDNs) and a latent variable method, the general topographic mapping (GTM), is compared. According to the results, MLPs and RBFs outperform MDNs and the GTM for these one-to-many mapping problems.
URI: http://ktisis.cut.ac.cy/handle/10488/7051
ISSN: 0941-0643
DOI: 10.1007/s00521-010-0483-4
Rights: © Springer-Verlag Berlin Heidelberg
Type: Article
Appears in Collections:Άρθρα/Articles

Show full item record

SCOPUSTM   
Citations 20

5
checked on Nov 24, 2017

WEB OF SCIENCETM
Citations 20

5
checked on Nov 23, 2017

Page view(s)

27
Last Week
1
Last month
1
checked on Nov 23, 2017

Google ScholarTM

Check

Altmetric


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