Zobrazeno 1 - 10
of 15
pro vyhledávání: '"André E. dos Santos"'
Autor:
Aurielle S. Medeiros, Mércia V. F. dos Santos, Márcio V. da Cunha, Alexandre C. L. de Mello, Djalma E. Simões Neto, Osniel F. de Oliveira, James P. Muir, Jose C. B. Dubeux, André E. dos Santos
Publikováno v:
Grass and Forage Science. 78:161-172
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5c712a5376914198d5f39a79ccf22cb3
https://doi.org/10.1111/gfs.12586
https://doi.org/10.1111/gfs.12586
Publikováno v:
International Journal of Approximate Reasoning. 92:270-278
Directed separation (d-separation) played a fundamental role in the founding of Bayesian networks (BNs) and continues to be useful today in a wide range of applications. Given an independence to be tested, current implementations of d-separation expl
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4a3fde8223e316b76379d1b78b640b40
https://doi.org/10.1016/j.ijar.2017.10.026
https://doi.org/10.1016/j.ijar.2017.10.026
Publikováno v:
Computational Intelligence. 34:789-801
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::64d5eddf79961131df757351f58c0cb5
https://doi.org/10.1111/coin.12130
https://doi.org/10.1111/coin.12130
Publikováno v:
Computational Intelligence. 33:629-655
We suggest Darwinian Networks (DNs) as a simplification of working with Bayesian networks (BNs). DNs adapt a handful of well-known concepts in biology into a single framework that is surprisingly simple yet remarkably robust. With respect to modeling
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1450395267e589e09be363fdcf111caa
https://doi.org/10.1111/coin.12101
https://doi.org/10.1111/coin.12101
Autor:
Guilherme Ourique Verran, Régis Kovacs Scalice, Danielle Bond, Anderson Tonello Bringhenti, André E. dos Santos, Juliana Ilha Zimmermann
Publikováno v:
Journal of the Brazilian Society of Mechanical Sciences and Engineering. 39:3151-3163
The design of casting components is a complex activity, which is usually based on guidelines scattered in the literature, or based on the designer’s accumulated experience. A single failure in the casting process selection can increase design and p
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::afe1d28f54b705b9444c46ed0ea4c220
https://doi.org/10.1007/s40430-017-0755-3
https://doi.org/10.1007/s40430-017-0755-3
Publikováno v:
Advances in Artificial Intelligence ISBN: 9783030183042
Canadian Conference on AI
Canadian Conference on AI
Recently, Simple Propagation was introduced as an algorithm for belief update in Bayesian networks using message passing in a junction tree. The algorithm differs from other message passing algorithms such as Lazy Propagation in the message construct
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::44f772d2e25cdf596560c0a7f82ed97b
https://doi.org/10.1007/978-3-030-18305-9_6
https://doi.org/10.1007/978-3-030-18305-9_6
Publikováno v:
Advances in Artificial Intelligence ISBN: 9783030183042
Canadian Conference on AI
Canadian Conference on AI
In this paper, we exploit the symmetry of independence in the implementation of d-separation. We show that it can matter whether the search is conducted from start to goal or vice versa. Analysis reveals it is preferable to approach observed v-struct
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::12553b9cf79628a31cab2823a78d3f0c
https://doi.org/10.1007/978-3-030-18305-9_4
https://doi.org/10.1007/978-3-030-18305-9_4
Publikováno v:
Butz, C J, Oliveira, J, dos Santos, A E & Madsen, A L 2018, ' An empirical study of Bayesian network inference with simple propagation ', International Journal of Approximate Reasoning, vol. 92, pp. 198-211 . https://doi.org/10.1016/j.ijar.2017.10.005
We propose Simple Propagation (SP) as a new join tree propagation algorithm for exact inference in discrete Bayesian networks. We establish the correctness of SP. The striking feature of SP is that its message construction exploits the factorization
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::41b710fe47ee5a7c1cacd746b00a50a6
https://vbn.aau.dk/da/publications/458af023-4cbc-40f6-99f8-57093de3bfc8
https://vbn.aau.dk/da/publications/458af023-4cbc-40f6-99f8-57093de3bfc8
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783319920573
IEA/AIE
IEA/AIE
This paper describes a novel approach to study bacterial relationships in soil datasets using probabilistic graphical models. We demonstrate how to access and reformat publicly available datasets in order to apply machine learning techniques. We firs
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3df2ae78ee91ddb0d220780a79b16e68
https://doi.org/10.1007/978-3-319-92058-0_30
https://doi.org/10.1007/978-3-319-92058-0_30
Publikováno v:
SSCI
LearnSPN is the standard unsupervised learning algorithm for sum-product networks (SPNs). It is based upon a “chop” operation for splitting features (columns) and a “slice” operation for clustering instances (rows). However, a number of techn
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7fdd0c42ac14a7784286dfe15236a5ae
https://doi.org/10.1109/ssci.2017.8280810
https://doi.org/10.1109/ssci.2017.8280810