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Mostrando ítems 21-30 de 20189
A novel decomposing model with evolutionary algorithms for feature selection in long non-coding rnas
(2020-01-01)
Machine learning algorithms have been applied to numerous transcript datasets to identify Long non-coding RNAs (lncRNAs). Nevertheless, before these algorithms are applied to RNA data, features must be extracted from the ...
Features-oriented model-driven architecture: uma abordagem para MDD
(Pontifícia Universidade Católica do Rio Grande do SulPorto Alegre, 2006)
O desenvolvimento de software dirigido por modelos, com a MDA, requer o refinamento de modelos de sistemas, inicialmente especificados em alto nível e sem características de plataformas, em modelos dependentes de plataformas. ...
Features-oriented model-driven architecture: uma abordagem para MDD
(Pontifícia Universidade Católica do Rio Grande do SulPorto Alegre, 2006)
O desenvolvimento de software dirigido por modelos, com a MDA, requer o refinamento de modelos de sistemas, inicialmente especificados em alto nível e sem características de plataformas, em modelos dependentes de plataformas. ...
An Approach Based on Feature Models and Quality Criteria for Adapting Component-Based Systems
(Springer, 2015-06)
Background: Feature modeling has been widely used in domain engineering for thedevelopment and configuration of software product lines. A feature model represents the set of possible products or configurations to apply in ...
WS-contract establishment with QoS: An approach based on feature modeling
(World Scientific Publ Co Pte LtdSingaporeSingapura, 2008)
Scalable 3D shape retrieval using local features and the signature quadratic form distance
(Springer, 2017)
We present a scalable and unsupervised approach for content-based retrieval on 3D model collections. Our goal is to represent a 3D shape as a set of discriminative local features, which is important to maintain robustness ...
Feature Selection for Privileged Modalities in Disease Classification
(2021-01-01)
Multimodal data allows supervised learning while considering multiple complementary views of a problem, improving final diagnostic performance of trained models. Data modalities that are missing or difficult to obtain in ...
A binary-constrained Geometric Semantic Genetic Programming for feature selection purposes
(Elsevier B.V., 2017-12-01)
Feature selection concerns the task of finding the subset of features that are most relevant to some specific problem in the context of machine learning. By selecting proper features, one can reduce the computational ...
Face recognition under pose variation with local Gabor features enhanced by Active Shape and Statistical Models
(Elsevier, 2015)
Face recognition is one of the most active areas of research in computer vision. Gabor features have been used widely in face identification because of their good results and robustness. However, the results of face ...
Feature Selection Using Geometric Semantic Genetic Programming
(Assoc Computing Machinery, 2017-01-01)
Feature selection concerns the task of finding the subset of features that are most relevant to some specific problem in the context of machine learning. During the last years, the problem of feature selection has been ...