Heterogeneous neural networks and the leader2 algorithm
This paper is the final document written to gather the impressions and conclusions which we have come to during the development of this master thesis. In this research project you will find the description of a new kind of artificial neural network, Heterogeneous Neural Network 2 (HNN2), which can b...
| Autor: | |
|---|---|
| Tipo de recurso: | tesis de maestría |
| Fecha de publicación: | 2010 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2099.1/11322 |
| Acceso en línea: | https://hdl.handle.net/2099.1/11322 |
| Access Level: | acceso abierto |
| Palabra clave: | Neural networks (Computer science) Expert systems (Computer science) Xarxes neuronals (Informàtica) Sistemes experts (Informàtica) Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Sistemes experts |
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Heterogeneous neural networks and the leader2 algorithmHernández González, JerónimoNeural networks (Computer science)Expert systems (Computer science)Xarxes neuronals (Informàtica)Sistemes experts (Informàtica)Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Sistemes expertsThis paper is the final document written to gather the impressions and conclusions which we have come to during the development of this master thesis. In this research project you will find the description of a new kind of artificial neural network, Heterogeneous Neural Network 2 (HNN2), which can be seen as a general abstraction of the Radial Basis Function network. The model of neuron used is an improved version of the one presented by Belanche [1] and the neural network is initialized using a clustering algorithm, Leader2, developed at [2]. We will explain the way we have followed to get this artificial neural network that works allways with understandable information, uses the concept of similarity and allows users to improve the algorithm results taking advantage of expert information. The basic Heterogeneous Neural Network (HNN) is also known as Similarity Neural Network (SNN), by the importance of the similarity measures inside this method. The basic idea is that a combination of similarity functions, comparing variables independently, is more capable of catching better the singularity of an heterogeneous data set than other methods which require previous data transformation. Each variable has its own characteristics, which is information that can be used by the expert that knows it to choose its most suitable similarity function, taking advantage of all the information he has. If this is done for each variable, we will be working probably with a similarity measure that understands better the data. Missing values are also a relevant characteristic of heterogeneous data, so we have to learn to deal with them. All these ideas are applied to HNN and Leader2, joint to several improvements performed to the neural network, like regularization or Alternate Optimization, in order to fit better the data but avoiding overfitting. This is why we have called it Heterogeneous Neural Network 2 (HNN2). This document is divided in several chapters. Initially, we will give an in-depth description of the problem which we want to solve. In the second chapter, State of the art, you will get a wide perspective of how was the field in which this project has been developed before we started. Then, there is a description of the used methodology, where you can find the main decisions and the development itself, followed by the explanation of the experimental settings done to test the HNN2. Their results are commented and evaluated in the next chapter, and next some conclusions are inferred. Finally, you will find the references used in the research and several annexes with additional relevant information. But in first term, before starting the description of the problem and in the way of making the reading easier, it is necessary to provide you some vocabulary to know exactly the meaning we have given to several key words. Next, in the same terms, you will find the most used symbols with their description.Universitat Politècnica de CatalunyaBelanche Muñoz, Luis Antonio20102010-09-0320112011-03-10master thesishttp://purl.org/coar/resource_type/c_bdccNAhttp://purl.org/coar/version/c_be7fb7dd8ff6fe43info:eu-repo/semantics/masterThesisapplication/pdfhttps://hdl.handle.net/2099.1/11322reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Attribution-NonCommercial-NoDerivs 3.0 Spainhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2099.1/113222026-05-27T15:37:01Z |
| dc.title.none.fl_str_mv |
Heterogeneous neural networks and the leader2 algorithm |
| title |
Heterogeneous neural networks and the leader2 algorithm |
| spellingShingle |
Heterogeneous neural networks and the leader2 algorithm Hernández González, Jerónimo Neural networks (Computer science) Expert systems (Computer science) Xarxes neuronals (Informàtica) Sistemes experts (Informàtica) Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Sistemes experts |
| title_short |
Heterogeneous neural networks and the leader2 algorithm |
| title_full |
Heterogeneous neural networks and the leader2 algorithm |
| title_fullStr |
Heterogeneous neural networks and the leader2 algorithm |
| title_full_unstemmed |
Heterogeneous neural networks and the leader2 algorithm |
| title_sort |
Heterogeneous neural networks and the leader2 algorithm |
| dc.creator.none.fl_str_mv |
Hernández González, Jerónimo |
| author |
Hernández González, Jerónimo |
| author_facet |
Hernández González, Jerónimo |
| author_role |
author |
| dc.contributor.none.fl_str_mv |
Belanche Muñoz, Luis Antonio |
| dc.subject.none.fl_str_mv |
Neural networks (Computer science) Expert systems (Computer science) Xarxes neuronals (Informàtica) Sistemes experts (Informàtica) Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Sistemes experts |
| topic |
Neural networks (Computer science) Expert systems (Computer science) Xarxes neuronals (Informàtica) Sistemes experts (Informàtica) Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Sistemes experts |
| description |
This paper is the final document written to gather the impressions and conclusions which we have come to during the development of this master thesis. In this research project you will find the description of a new kind of artificial neural network, Heterogeneous Neural Network 2 (HNN2), which can be seen as a general abstraction of the Radial Basis Function network. The model of neuron used is an improved version of the one presented by Belanche [1] and the neural network is initialized using a clustering algorithm, Leader2, developed at [2]. We will explain the way we have followed to get this artificial neural network that works allways with understandable information, uses the concept of similarity and allows users to improve the algorithm results taking advantage of expert information. The basic Heterogeneous Neural Network (HNN) is also known as Similarity Neural Network (SNN), by the importance of the similarity measures inside this method. The basic idea is that a combination of similarity functions, comparing variables independently, is more capable of catching better the singularity of an heterogeneous data set than other methods which require previous data transformation. Each variable has its own characteristics, which is information that can be used by the expert that knows it to choose its most suitable similarity function, taking advantage of all the information he has. If this is done for each variable, we will be working probably with a similarity measure that understands better the data. Missing values are also a relevant characteristic of heterogeneous data, so we have to learn to deal with them. All these ideas are applied to HNN and Leader2, joint to several improvements performed to the neural network, like regularization or Alternate Optimization, in order to fit better the data but avoiding overfitting. This is why we have called it Heterogeneous Neural Network 2 (HNN2). This document is divided in several chapters. Initially, we will give an in-depth description of the problem which we want to solve. In the second chapter, State of the art, you will get a wide perspective of how was the field in which this project has been developed before we started. Then, there is a description of the used methodology, where you can find the main decisions and the development itself, followed by the explanation of the experimental settings done to test the HNN2. Their results are commented and evaluated in the next chapter, and next some conclusions are inferred. Finally, you will find the references used in the research and several annexes with additional relevant information. But in first term, before starting the description of the problem and in the way of making the reading easier, it is necessary to provide you some vocabulary to know exactly the meaning we have given to several key words. Next, in the same terms, you will find the most used symbols with their description. |
| publishDate |
2010 |
| dc.date.none.fl_str_mv |
2010 2010-09-03 2011 2011-03-10 |
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master thesis http://purl.org/coar/resource_type/c_bdcc NA http://purl.org/coar/version/c_be7fb7dd8ff6fe43 |
| dc.type.openaire.fl_str_mv |
info:eu-repo/semantics/masterThesis |
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masterThesis |
| dc.identifier.none.fl_str_mv |
https://hdl.handle.net/2099.1/11322 |
| url |
https://hdl.handle.net/2099.1/11322 |
| dc.language.none.fl_str_mv |
Inglés eng |
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Inglés |
| language |
eng |
| dc.rights.none.fl_str_mv |
open access http://purl.org/coar/access_right/c_abf2 Attribution-NonCommercial-NoDerivs 3.0 Spain http://creativecommons.org/licenses/by-nc-nd/3.0/es/ |
| dc.rights.openaire.fl_str_mv |
info:eu-repo/semantics/openAccess |
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open access http://purl.org/coar/access_right/c_abf2 Attribution-NonCommercial-NoDerivs 3.0 Spain http://creativecommons.org/licenses/by-nc-nd/3.0/es/ |
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openAccess |
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application/pdf |
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Universitat Politècnica de Catalunya |
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Universitat Politècnica de Catalunya |
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reponame:UPCommons. Portal del coneixement obert de la UPC instname:Universitat Politècnica de Catalunya (UPC) |
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Universitat Politècnica de Catalunya (UPC) |
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UPCommons. Portal del coneixement obert de la UPC |
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15,301603 |