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pro vyhledávání: '"Sancho Caparrini, Fernando"'
Neural-symbolic approaches to machine learning incorporate the advantages from both connectionist and symbolic methods. Typically, these models employ a first module based on a neural architecture to extract features from complex data. Then, these fe
Externí odkaz:
http://arxiv.org/abs/2307.08087
Network embedding techniques inspired by word2vec represent an effective unsupervised relational learning model. Commonly, by means of a Skip-Gram procedure, these techniques learn low dimensional vector representations of the nodes in a graph by sam
Externí odkaz:
http://arxiv.org/abs/1907.08793
Autor:
Almagro-Blanco, Pedro1 (AUTHOR) palmagro@us.es, Sancho-Caparrini, Fernando1 (AUTHOR), Borrego-Díaz, Joaquín1 (AUTHOR) jborrego@us.es
Publikováno v:
Mathematics (2227-7390). Nov2023, Vol. 11 Issue 22, p4672. 22p.
A new approach to the study of Generalized Graphs as semantic data structures using machine learning techniques is presented. We show how vector representations maintaining semantic characteristics of the original data can be obtained from a given gr
Externí odkaz:
http://arxiv.org/abs/1709.02759
Usually, decision tree induction algorithms are limited to work with non relational data. Given a record, they do not take into account other objects attributes even though they can provide valuable information for the learning task. In this paper we
Externí odkaz:
http://arxiv.org/abs/1708.05563
Most of the machine learning algorithms are limited to learn from flat data: a recordset with prefixed structure. When learning from a record, these types of algorithms don't take into account other objects even though they are directly connected to
Externí odkaz:
http://arxiv.org/abs/1708.03734
Autor:
López-Ortiz, Enrique J., Sancho-Caparrini, Fernando, Martínez-del-Amor, Miguel Á., Soria-Morillo, Luis M., Álvarez-García, Juan A.
Publikováno v:
In Knowledge-Based Systems 21 June 2021 222
Autor:
Salazar González, Jose L., Zaccaro, Carlos, Álvarez-García, Juan A., Soria Morillo, Luis M., Sancho Caparrini, Fernando
Publikováno v:
In Neural Networks December 2020 132:297-308
Autor:
Sancho Caparrini, Fernando1 fsancho@us.es
Publikováno v:
Universitas Humanística. jul-dic2020, Issue 90, p1-12. 12p.
Autor:
Li, Chuan, Cabrera, Diego, Sancho Caparrini, Fernando, Cerrada, Mariela, Sánchez, René-Vinicio, Estupinan, Edgar
Publikováno v:
idUS. Depósito de Investigación de la Universidad de Sevilla
instname
idUS: Depósito de Investigación de la Universidad de Sevilla
Universidad de Sevilla (US)
instname
idUS: Depósito de Investigación de la Universidad de Sevilla
Universidad de Sevilla (US)
The lack of faulty condition data reduces the feasibility of supervised learning for fault detection or fault severity discrimination in new manufacturing technologies. To deal with this issue, one-class learning arises for building binary discrimina
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::01344949b253d1414de13acdd42330ee