Zobrazeno 1 - 10
of 23
pro vyhledávání: '"Karina Vilches"'
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-14 (2024)
Abstract Randomization-based neural networks have gained wide acceptance in the scientific community owing to the simplicity of their algorithm and generalization capabilities. Random vector functional link (RVFL) networks and their variants are a cl
Externí odkaz:
https://doaj.org/article/f6c1c0761d1a489481ca3690a41b8d6c
Autor:
Elkin Gelvez-Almeida, Marco Mora, Ricardo J. Barrientos, Ruber Hernández-García, Karina Vilches-Ponce, Miguel Vera
Publikováno v:
Mathematical and Computational Applications, Vol 29, Iss 3, p 40 (2024)
The randomization-based feedforward neural network has raised great interest in the scientific community due to its simplicity, training speed, and accuracy comparable to traditional learning algorithms. The basic algorithm consists of randomly deter
Externí odkaz:
https://doaj.org/article/ac5c2cbca04c49939e4d7d1c7cf51916
Publikováno v:
IEEE Access, Vol 11, Pp 134834-134845 (2023)
The computation of the Moore–Penrose generalized inverse is a commonly used operation in various fields such as the training of neural networks based on random weights. Therefore, a fast computation of this inverse is important for problems where s
Externí odkaz:
https://doaj.org/article/b6166452959a482c958b2bd689ec6aec
Autor:
Juan Pablo Gutiérrez-Jara, Katia Vogt-Geisse, Margarita C. G. Correa, Karina Vilches-Ponce, Laura M. Pérez, Gerardo Chowell
Publikováno v:
Plants, Vol 12, Iss 19, p 3442 (2023)
Sharka is a disease affecting stone fruit trees. It is caused by the Plum pox virus (PPV), with Myzus persicae being one of the most efficient aphid species in transmitting it within and among Prunus orchards. Other agricultural management strategies
Externí odkaz:
https://doaj.org/article/93fadd695b694dd9bbab9744aec2df76
Publikováno v:
Applied Sciences, Vol 12, Iss 18, p 9021 (2022)
Orthogonal transformations, proper decomposition, and the Moore–Penrose inverse are traditional methods of obtaining the output layer weights for an extreme learning machine autoencoder. However, an increase in the number of hidden neurons causes h
Externí odkaz:
https://doaj.org/article/73ef4c9626284eefbfd6f777648dce1a
Autor:
Muñoz-Quezada, María Teresa a, ⁎, Lucero, Boris A. a, Gutiérrez-Jara, Juan Pablo b, Buralli, Rafael J. c, Zúñiga-Venegas, Liliana a, b, d, Muñoz, María Pía e, Ponce, Karina Vilches f, Iglesias, Verónica e
Publikováno v:
In Science of the Total Environment 20 December 2020 749
Publikováno v:
Artificial Intelligence Review.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8022a035a83f46ab79e60db43a21dfe5
https://doi.org/10.1007/s10462-023-10478-4
https://doi.org/10.1007/s10462-023-10478-4
Publikováno v:
2022 41st International Conference of the Chilean Computer Science Society (SCCC).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b72b519db0f691a5f3e9058e67f6ff8f
https://doi.org/10.1109/sccc57464.2022.10000361
https://doi.org/10.1109/sccc57464.2022.10000361
Autor:
Jose A. Vasquez-Coronel, Marco Mora, Karina Vilches, Fabian Silva-Pavez, Italo Torres-Gonzalez, Pedro Barria-Valdevenito
Publikováno v:
2022 IEEE International Conference on Automation/XXV Congress of the Chilean Association of Automatic Control (ICA-ACCA).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2ec2df36f951428c4c284647fd296a73
https://doi.org/10.1109/ica-acca56767.2022.10006276
https://doi.org/10.1109/ica-acca56767.2022.10006276
Publikováno v:
Applied Sciences; Volume 12; Issue 18; Pages: 9021
Orthogonal transformations, proper decomposition, and the Moore–Penrose inverse are traditional methods of obtaining the output layer weights for an extreme learning machine autoencoder. However, an increase in the number of hidden neurons causes h
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c7d146784d0350cab919f36d16a37410