dc.contributor.author | Jayne, C. | en |
dc.contributor.author | Lanitis, A. | en |
dc.contributor.author | Christodoulou, Chris C. | en |
dc.creator | Jayne, C. | en |
dc.creator | Lanitis, A. | en |
dc.creator | Christodoulou, Chris C. | en |
dc.date.accessioned | 2019-11-13T10:40:25Z | |
dc.date.available | 2019-11-13T10:40:25Z | |
dc.date.issued | 2011 | |
dc.identifier.uri | http://gnosis.library.ucy.ac.cy/handle/7/54112 | |
dc.description.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. © 2010 Springer-Verlag London Limited. | en |
dc.source | Neural Computing and Applications | en |
dc.source.uri | https://www.scopus.com/inward/record.uri?eid=2-s2.0-80051670959&doi=10.1007%2fs00521-010-0483-4&partnerID=40&md5=e15384bc3b6b9300bb9c480b7c3b9908 | |
dc.subject | Radial basis functions | en |
dc.subject | Forecasting | en |
dc.subject | Neural networks | en |
dc.subject | Mapping | en |
dc.subject | Ill-conditioned | en |
dc.subject | Radial basis function networks | en |
dc.subject | Input parameter | en |
dc.subject | Pattern recognition systems | en |
dc.subject | Multilayer neural networks | en |
dc.subject | Multivariant analysis | en |
dc.subject | Multivariate statistics | en |
dc.subject | Complex mapping | en |
dc.subject | General index | en |
dc.subject | Latent variable methods | en |
dc.subject | Mixture density | en |
dc.subject | Network-based | en |
dc.subject | Problem domain | en |
dc.subject | Standard neural | en |
dc.subject | Topographic mapping | en |
dc.subject | Neural network method | en |
dc.subject | One-to-many mapping | en |
dc.subject | Exam grades prediction | en |
dc.subject | High school | en |
dc.subject | Multiple parameters | en |
dc.subject | Multivalued mappings | en |
dc.subject | Multivariate distributions | en |
dc.subject | Single parameter | en |
dc.subject | Single source | en |
dc.subject | Single variable | en |
dc.subject | Sources of variation | en |
dc.subject | Stock price prediction | en |
dc.subject | Stock shares | en |
dc.title | Neural network methods for one-to-many multi-valued mapping problems | en |
dc.type | info:eu-repo/semantics/article | |
dc.identifier.doi | 10.1007/s00521-010-0483-4 | |
dc.description.volume | 20 | |
dc.description.issue | 6 | |
dc.description.startingpage | 775 | |
dc.description.endingpage | 785 | |
dc.author.faculty | 002 Σχολή Θετικών και Εφαρμοσμένων Επιστημών / Faculty of Pure and Applied Sciences | |
dc.author.department | Τμήμα Πληροφορικής / Department of Computer Science | |
dc.type.uhtype | Article | en |
dc.description.notes | <p>Cited By :5</p> | en |
dc.source.abbreviation | Neural Comput.Appl. | en |
dc.contributor.orcid | Christodoulou, Chris C. [0000-0001-9398-5256] | |
dc.gnosis.orcid | 0000-0001-9398-5256 | |