Paternain Dallo, Daniel
Loading...
Email Address
person.page.identifierURI
Birth Date
Job Title
Last Name
Paternain Dallo
First Name
Daniel
person.page.departamento
Estadística, Informática y Matemáticas
person.page.instituteName
ISC. Institute of Smart Cities
ORCID
person.page.observainves
person.page.upna
Name
- Publications
- item.page.relationships.isAdvisorOfPublication
- item.page.relationships.isAdvisorTFEOfPublication
- item.page.relationships.isAuthorMDOfPublication
2 results
Search Results
Now showing 1 - 2 of 2
Publication Open Access A study of OWA operators learned in convolutional neural networks(MDPI, 2021) Domínguez Catena, Iris; Paternain Dallo, Daniel; Galar Idoate, Mikel; Institute of Smart Cities - ISC; Universidad Pública de Navarra / Nafarroako Unibertsitate PublikoaOrdered Weighted Averaging (OWA) operators have been integrated in Convolutional Neural Networks (CNNs) for image classification through the OWA layer. This layer lets the CNN integrate global information about the image in the early stages, where most CNN architectures only allow for the exploitation of local information. As a side effect of this integration, the OWA layer becomes a practical method for the determination of OWA operator weights, which is usually a difficult task that complicates the integration of these operators in other fields. In this paper, we explore the weights learned for the OWA operators inside the OWA layer, characterizing them through their basic properties of orness and dispersion. We also compare them to some families of OWA operators, namely the Binomial OWA operator, the Stancu OWA operator and the expo-nential RIM OWA operator, finding examples that are currently impossible to generalize through these parameterizations.Publication Open Access Orness measurements for lattice m-dimensional interval-valued OWA operators(Elsevier, 2018) Miguel Turullols, Laura de; Paternain Dallo, Daniel; Lizasoain Iriso, María Inmaculada; Ochoa Lezaun, Gustavo; Bustince Sola, Humberto; Estatistika, Informatika eta Matematika; Institute for Advanced Materials and Mathematics - INAMAT2; Institute of Smart Cities - ISC; Estadística, Informática y MatemáticasOrdered weighted average (OWA) operators are commonly used to aggregate information in multiple situations, such as decision making problems or image processing tasks. The great variety of weights that can be chosen to determinate an OWA operator provides a broad family of aggegating functions, which obviously give diferent results in the aggregation of the same set of data. In this paper, some possible classifications of OWA operators are suggested when they are de ned on m-dimensional intervals taking values on a complete lattice satisfying certain local conditions. A first classification is obtained by means of a quantitative orness measure that gives the proximity of each OWA to the OR operator. In the case in which the lattice is finite, another classification is obtained by means of a qualitative orness measure. In the present paper, several theoretical results are obtained in order to perform this qualitative value for each OWA operator.