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| Sousa,Ithalo Coelho de; Nascimento,Moysés; Silva,Gabi Nunes; Nascimento,Ana Carolina Campana; Cruz,Cosme Damião; Silva,Fabyano Fonseca e; Almeida,Dênia Pires de; Pestana,Kátia Nogueira; Azevedo,Camila Ferreira; Zambolim,Laércio; Caixeta,Eveline Teixeira. |
| ABSTRACT Genomic selection (GS) emphasizes the simultaneous prediction of the genetic effects of thousands of scattered markers over the genome. Several statistical methodologies have been used in GS for the prediction of genetic merit. In general, such methodologies require certain assumptions about the data, such as the normality of the distribution of phenotypic values. To circumvent the non-normality of phenotypic values, the literature suggests the use of Bayesian Generalized Linear Regression (GBLASSO). Another alternative is the models based on machine learning, represented by methodologies such as Artificial Neural Networks (ANN), Decision Trees (DT) and related possible refinements such as Bagging, Random Forest and Boosting. This study aimed to... |
| Tipo: Info:eu-repo/semantics/article |
Palavras-chave: Hemileia vastatrix; Statistical learning; Plant breeding; Artificial intelligence. |
| Ano: 2021 |
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162021000401102 |
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| Fablet, Ronan. |
| Computer-assisted tools need to be developed to help in the accurate and efficient acquisition of fish age and growth data for ecological and assessment issues. Stating fish age and growth analysis as pattern classification issues, the proposed approach relies on a statistical learning strategy. Given otolith images interpreted by an expert, probabilistic kernel-based methods (namely Kernel Logistic Regression) are used to infer interpretation rules. More precisely, two different probabilistic models are introduced: one to infer fish age from otolith images and a second one aiming at evaluating whether or not a given otolith growth pattern is realistic w.r.t. training examples. These probabilistic models provide us with the basis for coping with three... |
| Tipo: Text |
Palavras-chave: Computer assisted fish age and growth analysis; Otolith image analysis; Otolith interpretation; Statistical learning. |
| Ano: 2006 |
URL: http://archimer.ifremer.fr/doc/2006/publication-2136.pdf |
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