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A feature fusion method for feature extraction
SPIE Proceedings, 2012The automatic target recognition based on image fusion refers to the fusion process using the target images provided by a variety of sensors, so as to improve the recognition accuracy and robustness and to obtain better recognition performance.
Dejun Tang +3 more
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Regularization and feature selection for networked features
Proceedings of the 19th ACM international conference on Information and knowledge management, 2010In the standard formalization of supervised learning problems, a datum is represented as a vector of features without prior knowledge about relationships among features. However, for many real world problems, we have such prior knowledge about structure relationships among features. For instance, in Microarray analysis where the genes are features, the
Hongliang Fei, Brian Quanz, Jun Huan
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Feature modelling by incremental feature recognition
Computer-Aided Design, 1993Abstract A novel feature-modelling system which implements a hybrid of feature-based design and feature recognition in a single framework is described. During the design process of a part, the user can modify interactively either the solid model or the feature model of the part while the system keeps the other model 3onsistent with the changed one ...
Timo Laakko, Martti Mäntylä
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Feature vector field and feature matching
Pattern Recognition, 2010zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Fuchao Wu, Zhiheng Wang, Xuguang Wang
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Feature conversion between neutral features and application features
Computers & Industrial Engineering, 1995Abstract The mapping of design intent to the subsequent downstream application features, such as machining planning, setup planning, engineering analysis, assembly planning, etc., are important areas of research in CIM. This paper describes a prototype feature conversion system.
Leung, CB, Wong, TN
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Ensemble Feature Selection for Rankings of Features
2015In the last few years, ensemble learning has been the focus of much attention mainly in classification tasks, based on the assumption that combining the output of multiple experts is better than the output of any single expert. This idea of ensemble learning can be adapted for feature selection, in which different feature selection algorithms act as ...
Borja Seijo-Pardo +3 more
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Feature Usage Diagram for Feature Reduction
2013Feature creep, if not managed well, cause software bloat. This in turn makes software applications become slower. Currently, software industry urgently requires mechanisms and approaches to reduce unnecessary or low value features. In this paper, we introduce a modelling notation, so called Feature Usage Diagram, and an approach to identify and ...
Sarunas Marciuska +3 more
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Feature constraints with first-class features
1993Feature Constraint Systems have been proposed as a logical data structure for constraint (logic) programming. They provide a record-like view to trees by identifying subtrees by keyword rather than by position. Their atomic constraints are finer grained than in the constructor-based approach.
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Combining Feature Subsets in Feature Selection
2005In feature selection, a part of the features is chosen as a new feature subset, while the rest of the features is ignored. The neglected features still, however, may contain useful information for discriminating the data classes. To make use of this information, the combined classifier approach can be used.
Marina Skurichina, Robert P. W. Duin
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Feature Ranking for Feature Sorting and Feature Selection with Optimisation
2023Paola Santana-Morales +6 more
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