Relevant bibliographies by topics / Feature-based

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Feature-based'

Author: Grafiati
Published: 4 June 2021
Last updated: 15 June 2025

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Journal articles on the topic "Feature-based"

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Summerfield, Christopher, and Tobias Egner. "Feature-Based Attention and Feature-Based Expectation." Trends in Cognitive Sciences 20, no. 6 (2016): 401–4. http://dx.doi.org/10.1016/j.tics.2016.03.008.

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Apel, Sven, Alexander von Rhein, Thomas Thüm, and Christian Kästner. "Feature-interaction detection based on feature-based specifications." Computer Networks 57, no. 12 (2013): 2399–409. http://dx.doi.org/10.1016/j.comnet.2013.02.025.

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Ramchandra, Ambika, and Ravindra kumar. "Algorithms of Feature Based and Image Based Face Recognition." Indian Journal of Applied Research 3, no. 12 (2011): 128–30. http://dx.doi.org/10.15373/2249555x/dec2013/34.

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Lei, Yan, Huan Xie, Tao Zhang, Meng Yan, Zhou Xu, and Chengnian Sun. "Feature-FL: Feature-Based Fault Localization." IEEE Transactions on Reliability 71, no. 1 (2022): 264–83. http://dx.doi.org/10.1109/tr.2022.3140453.

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Ozturk, F., and N. Ozturk. "Feature-based environmental issues: neural network-based feature recognition." International Journal of Vehicle Design 21, no. 2/3 (1999): 190. http://dx.doi.org/10.1504/ijvd.1999.005576.

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Ming-liang Gao, Ming-liang Gao, Xiaomin Yang Xiaomin Yang, Yanmei Yu Yanmei Yu, and Daisheng Luo Daisheng Luo. "Photometric invariant feature descriptor based on SIFT." Chinese Optics Letters 10, s1 (2012): S11003–311008. http://dx.doi.org/10.3788/col201210.s11003.

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Ashokkumar Lodha, Nishakumari, and Minakshi Somanath Bagad. "Review for Feature Based Image Re-Ranking." International Journal of Scientific Engineering and Research 3, no. 6 (2015): 43–55. https://doi.org/10.70729/ijser15224.

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Somanath Bagad, Minakshi, and Sonal P Patil. "Feature Based Image Reranking Using Fusion Weights." International Journal of Scientific Engineering and Research 4, no. 3 (2016): 71–74. https://doi.org/10.70729/ijser15716.

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Farah, Qasim Ahmed Alyousuf, and Din Roshidi. "Analysis review on feature-based and word-rule based techniques in text steganography." Bulletin of Electrical Engineering and Informatics 9, no. 2 (2020): 764–70. https://doi.org/10.11591/eei.v9i2.2069.

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This paper presents several techniques used in text steganography in term of feature-based and word-rule based. Additionally, it analyses the performance and the metric evaluation of the techniques used in text steganography. This paper aims to identify the main techniques of text steganography, which are feature-based, and word-rule based, to recognize the various techniques used with them. As a result, the primary technique used in the text steganography was feature-based technique due to its simplicity and secured. Meanwhile, the common parameter metrics utilized in text steganography were security, capacity, robustness, and embedding time. Future efforts are suggested to focus on the methods used in text steganography.
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Sloman, S. A. "Feature-Based Induction." Cognitive Psychology 25, no. 2 (1993): 231–80. http://dx.doi.org/10.1006/cogp.1993.1006.

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Dissertations / Theses on the topic "Feature-based"

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Zheng, Ling. "Feature grouping-based feature selection." Thesis, Aberystwyth University, 2017. http://hdl.handle.net/2160/41e7b226-d8e1-481f-9c48-4983f64b0a92.

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Feature selection (FS) is a process which aims to select input domain features that are most informative for a given outcome. Unlike other dimensionality reduction techniques, feature selection methods preserve the underlying semantics or meaning of the original data following reduction. Typically, FS can be divided into four categories: filter, wrapper, hybrid-based and embedded approaches. Many strategies have been proposed for this task in an effort to identify more compact and better quality feature subsets. As various advanced techniques have emerged in the development of search mechanisms, it has become increasingly possible for quality feature subsets to be discovered efficiently without resorting to exhaustive search. Harmony search is a music-inspired stochastic search method. This general technique can be used to support FS in conjunction with many available feature subset quality evaluation methods. The structural simplicity of this technique means that it is capable of reducing the overall complexity of the subset search. The naturally stochastic properties of this technique also help to reduce local optima for any resultant feature subset, whilst locating multiple, potential candidates for the final subset. However, it is not sufficiently flexible in adjusting the size of the parametric musician population, which directly affects the performance on feature subset size reduction. This weakness can be alleviated to a certain extent by an iterative refinement extension, but the fundamental issue remains. Stochastic mechanisms have not been explored to their maximum potential by the original work, as it does not employ a parameter of pitch adjustment rate due to its ineffective mapping of concepts. To address the above problems, this thesis proposes a series of extensions. Firstly, a self-adjusting approach is proposed for the task of FS which involves a mechanism to further improve the performance of the existing harmony search-based method. This approach introduces three novel techniques: a restricted feature domain created for each individual musician contributing to the harmony improvisation in order to improve harmony diversity; a harmony memory consolidation which explores the possibility of exchanging/communicating information amongst musicians such that it can dynamically adjust the population of musicians in improvising new harmonies; and a pitch adjustment which exploits feature similarity measures to identify neighbouring features in order to fine-tune the newly discovered harmonies. These novel developments are also supplemented by a further new proposal involving the application to a feature grouping-based approach proposed herein for FS, which works by searching for feature subsets across homogeneous feature groups rather than examining a massive number of possible combinations of features. This approach radically departs from the traditional FS techniques that work by incrementally adding/removing features from a candidate feature subset one feature at a time or randomly selecting feature combinations without considering the relationship(s) between features. As such, information such as inter-feature correlation may be retained and the residual redundancy in the returned feature subset minimised. Two different instantiations of an FS mechanism are derived from such a feature grouping-based framework: one based upon the straightforward ranking of features within the resultant feature grouping; and the other on the simplification for harmony search-based FS. Feature grouping-based FS offers a self-adjusting approach to effectively and efficiently addressing many real-world problems which may have data dimensionality concerns and which requires semantic-preserving in data reduction. This thesis investigate the application of this approach in the area of intrusion detection, which must deal in a timely fashion with huge quantities of data extracted from network traffic or audit trails. This approach empirically demonstrates the efficacy of feature grouping-based FS in action.
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Chiba, Naoki. "Feature-Based Image Mosaicing." 京都大学 (Kyoto University), 2001. http://hdl.handle.net/2433/150613.

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Archambault, Daniel William. "Feature-based graph visualization." Thesis, University of British Columbia, 2008. http://hdl.handle.net/2429/2839.

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A graph consists of a set and a binary relation on that set. Each element of the set is a node of the graph, while each element of the binary relation is an edge of the graph that encodes a relationship between two nodes. Graph are pervasive in many areas of science, engineering, and the social sciences: servers on the Internet are connected, proteins interact in large biological systems, social networks encode the relationships between people, and functions call each other in a program. In these domains, the graphs can become very large, consisting of hundreds of thousands of nodes and millions of edges. Graph drawing approaches endeavour to place these nodes in two or three-dimensional space with the intention of fostering an understanding of the binary relation by a human being examining the image. However, many of these approaches to drawing do not exploit higher-level structures in the graph beyond the nodes and edges. Frequently, these structures can be exploited for drawing. As an example, consider a large computer network where nodes are servers and edges are connections between those servers. If a user would like understand how servers at UBC connect to the rest of the network, a drawing that accentuates the set of nodes representing those servers may be more helpful than an approach where all nodes are drawn in the same way. In a feature-based approach, features are subgraphs exploited for the purposes of drawing. We endeavour to depict not only the binary relation, but the high-level relationships between features. This thesis extensively explores a feature-based approach to graph vi sualization and demonstrates the viability of tools that aid in the visual ization of large graphs. Our contributions lie in presenting and evaluating novel techniques and algorithms for graph visualization. We implement five systems in order to empirically evaluate these techniques and algorithms, comparing them to previous approaches.
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Corney, Jonathan Roy. "Graph-based feature recognition." Thesis, Heriot-Watt University, 1993. http://hdl.handle.net/10399/1459.

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Smith, Stephen Mark. "Feature based image sequence understanding." Thesis, University of Oxford, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.316951.

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Adams, Daniel B. "Feature-based Interactive Terrain Sketching." BYU ScholarsArchive, 2009. https://scholarsarchive.byu.edu/etd/2288.

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Procedural generation techniques are able to quickly and cheaply produce large areas of terrain. However, these techniques produce results that are not easily directable and often require artists to edit the results by hand to achieve the desired layout. This paper proposes a sketch-based system for controlling fractal terrain that allows for a wide variety of terrain feature types. Artists sketch features rather than constrained points or elevations. The system is interactive, provides quick on-demand previews of the terrain, and allows for iterative design modifications. Interaction between features is handled in a realistic fashion. An arbitrary vertex insertion order midpoint displacement algorithm is also described which provides the necessary flexibility and constraints for the terrain generation system.
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Nabdel, Leili. "An Xml-based Feature Modeling Language." Master's thesis, METU, 2011. http://etd.lib.metu.edu.tr/upload/12613827/index.pdf.

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Feature modeling is a common way of representing commonality and variability in Software Product Lines. There are alternative notations reported in the literature to represent feature models. Compared to the graphical notations, the text-based notations are more amenable to automated processing and tool interoperability. This study presents an XML-based feature modeling language to represent extended feature models that can include complex relationships involving attributes. We first provide a Context Free Grammar for the extended feature model definitions including such complex relationships. Then we build the XML Schema Definitions and present a number of XML instances in accordance with the defined schema. In addition, we discuss a validation process for the validation of the XML instances against the defined schema, which also includes additional tasks such as well-formedness checking for the XML instances.
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Naing, Soe. "Feature based design for jigless assembly." Thesis, Cranfield University, 2004. http://dspace.lib.cranfield.ac.uk/handle/1826/106.

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The work presented in this thesis was undertaken as part of the three-year ‘Jigless Aerospace Manufacture’ (JAM) project which was set-up to investigate and address the significant scientific, technological and economic issues to enable a new design, manufacture and assembly philosophy based on minimising product specific jigs, fixtures and tooling. The main goal of the JAM project at Cranfield was the development of appropriate jigless methods and principles, and the subsequent redesign of the JAM project demonstrator structure – a section of the Airbus A320 aircraft Fixed Leading Edge – to fully investigate and realise the capabilities of jigless methodologies and principles. The particular focus of research activity described in this thesis was the development of a methodology to design for jigless assembly and a process of selecting assembly features to enable jigless assembly. A review of the literature has shown that no methodologies exist to specifically design for jigless assembly; however, previous relevant research has been built upon and extended with the incorporation of novel tools and techniques. To facilitate the assembly feature selection process for jigless assembly, an Assembly Feature Library was created that broadened and expanded the conventional definition and use of assembly features. The developed methodology, assembly feature selection process and Feature Library have been applied and validated on the JAM project demonstrator structure to serve as a Case Study for the tools and techniques developed by the research. Additionally, a Costing Analysis was carried out which suggests that the use of the tools and techniques to enable jigless assembly could have a large and considerable impact on both the Non-Recurring and Recurring costs associated with the design, manufacture and assembly of aircraft.
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Sze, Wui-fung. "Robust feature-point based image matching." Click to view the E-thesis via HKUTO, 2006. http://sunzi.lib.hku.hk/hkuto/record/B37153262.

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Sze, Wui-fung, and 施會豐. "Robust feature-point based image matching." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2006. http://hub.hku.hk/bib/B37153262.

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Books on the topic "Feature-based"

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Grant, Robin. Feature based image metamorphosis. University of Manchester, Departmentof Computer Science, 1996.

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Wright, Martin D. Feature based modelling for machining. typescript, 1998.

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Bennett, P. A. Feature-based approaches to grammar. UMIST, 1995.

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J, Shah Jami, Mäntylä Martti 1955-, and Nau Dana S, eds. Advances in feature based manufacturing. Elsevier, 1994.

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Soenen, René, and Gustav J. Olling, eds. Feature Based Product Life-Cycle Modelling. Springer US, 2003. http://dx.doi.org/10.1007/978-0-387-35637-2.

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Patil, D. Feature based computer aided process planning. typescript, 1995.

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Iskra, Dorota Jolanta. Feature-based approach to speech recognition. University of Birmingham, 2000.

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De Silva, Anthony Mihirana, and Philip H. W. Leong. Grammar-Based Feature Generation for Time-Series Prediction. Springer Singapore, 2015. http://dx.doi.org/10.1007/978-981-287-411-5.

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Langley, Christopher Stewart. Feature-based pose-estimation for robotic fixtureless assembly. National Library of Canada, 2001.

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Soenen, R. Feature Based Product Life-Cycle Modelling: IFIP TC5. Springer US, 2003.

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Book chapters on the topic "Feature-based"

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Szeliski, Richard. "Feature-based alignment." In Texts in Computer Science. Springer London, 2010. http://dx.doi.org/10.1007/978-1-84882-935-0_6.

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Metzler, Donald. "Feature-Based Ranking." In A Feature-Centric View of Information Retrieval. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22898-8_3.

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Blevins, James P. "Feature-Based Grammar." In Non-Transformational Syntax. Wiley-Blackwell, 2011. http://dx.doi.org/10.1002/9781444395037.ch8.

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Toews, Matthew, William M. Wells, D. Louis Collins, and Tal Arbel. "Feature-Based Morphometry." In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2009. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04271-3_14.

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Wanas, Nayer M., and Mohamed S. Kamel. "Feature Based Decision Fusion." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-44732-6_18.

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Padhy, Sisir K., and Suren N. Dwivedi. "Feature Based Design Assembly." In CAD/CAM Robotics and Factories of the Future ’90. Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/978-3-642-84338-9_28.

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De Silva, Anthony Mihirana, and Philip H. W. Leong. "Grammar Based Feature Generation." In SpringerBriefs in Applied Sciences and Technology. Springer Singapore, 2015. http://dx.doi.org/10.1007/978-981-287-411-5_4.

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Lorenz, Cristian, Tobias Klinder, and Jens von Berg. "Feature-Based Registration Techniques." In 4D Modeling and Estimation of Respiratory Motion for Radiation Therapy. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-36441-9_5.

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Shin, Kilho, and Xian Ming Xu. "Consistency-Based Feature Selection." In Knowledge-Based and Intelligent Information and Engineering Systems. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04595-0_42.

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Elibol, Armağan, Nuno Gracias, and Rafael Garcia. "Feature-Based Image Mosaicing." In Efficient Topology Estimation for Large Scale Optical Mapping. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-30313-5_2.

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Conference papers on the topic "Feature-based"

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Liang, Siyi, Yifan Lu, Yang Zhang, Yu Bai, and Kun Zheng. "Explainable Feature Selection Based on Feature Importance Analysis." In 2025 7th International Conference on Software Engineering and Computer Science (CSECS). IEEE, 2025. https://doi.org/10.1109/csecs64665.2025.11009527.

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Geelink, Reinholt, Otto W. Salomons, Fjodor van Slooten, Fred J. A. M. van Houten, and Huub J. J. Kals. "Unified Feature Definition for Feature Based Design and Feature Based Manufacturing." In ASME 1995 15th International Computers in Engineering Conference and the ASME 1995 9th Annual Engineering Database Symposium collocated with the ASME 1995 Design Engineering Technical Conferences. American Society of Mechanical Engineers, 1995. http://dx.doi.org/10.1115/cie1995-0785.

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Abstract In this paper, interactive “constraint based feature definition” is used to drive both feature based design and feature recognition. At present, hardly any feature based CAD or CAPP system does offer adequate facilities to easily define application specific features. Feature definition by means of programming is an error prone and difficult task. The definition of new features has to be performed by domain experts in the fields of design and manufacturing. In general they will not be programming experts. This paper elaborates on interactive feature definition, aiming at facilitating the definition of features by non-programming experts. The interactive feature definition functionality is implemented in a re-design support system called FROOM. It supports feature based design. Feature definition is also used in a Computer Aided Process Planning system, called PART, for the definition of features to be recognized. Conceptual graphs are used as an aid in the definition of features and for the representation of the features. The conceptual graphs are automatically transformed into feature recognition algorithms. Degrees of freedom (DOF) analysis is used for support during feature definition and for solving geometric constraints related to the feature to be defined.
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Feng Zhang, Ya-Jun Zhao, and Jun-Fen. "Unsupervised feature selection based on feature relevance." In 2009 Eighth International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2009. http://dx.doi.org/10.1109/icmlc.2009.5212453.

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Ibrahim, Marwa Farouk Ibrahim, and Adel Ali Al-jumaily. "ICA based feature learning and feature selection." In 2016 5th International Conference on Electronic Devices, Systems and Applications (ICEDSA). IEEE, 2016. http://dx.doi.org/10.1109/icedsa.2016.7818563.

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Jensen, Richard, Neil Mac Parthalain, and Chris Cornells. "Feature grouping-based fuzzy-rough feature selection." In 2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2014. http://dx.doi.org/10.1109/fuzz-ieee.2014.6891692.

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Ni, Jinxia, and Zhongxi Sun. "Feature extraction using Gabor feature-based IFDA." In 2011 2nd International Conference on Intelligent Control and Information Processing (ICICIP). IEEE, 2011. http://dx.doi.org/10.1109/icicip.2011.6008225.

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Krieger, Hans-Ulrich, Hannes Pirker, and John Nerbonne. "Feature-based allomorphy." In the 31st annual meeting. Association for Computational Linguistics, 1993. http://dx.doi.org/10.3115/981574.981593.

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Ibrahim, Marwa Farouk Ibrahim, and Adel Ali Al-Jumaily. "PCA indexing based feature learning and feature selection." In 2016 8th Cairo International Biomedical Engineering Conference (CIBEC). IEEE, 2016. http://dx.doi.org/10.1109/cibec.2016.7836122.

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Gao, Shuming, Zhengming Chen, and Qunsheng Peng. "Feature Validity Maintaining Based on Local Feature Recognition." In ASME 2000 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2000. http://dx.doi.org/10.1115/detc2000/cie-14644.

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Abstract Lack of feature validity maintenance function is recognized as a serious problem with current feature modeling system, which makes feature modeling just like an advanced geometric modeling with parametric and constraint-based modeling facilities. In this paper, we present a novel approach to feature validity maintaining. The approach detects the topologically destroyed features and determine the new states of the destroyed features by performing local feature recognition on the area of the part which consists of all the boundary entities of the edited feature and the features intersecting with the edited feature after a feature is edited. In the approach, feature conversion and feature mergence are distinguished from the real destruction of feature validity to let the user have chances to determine if the feature conversion or feature mergence is what he/she wants. To make the implementation of the approach easy, a representation for topologic validity conditions based on the extended attributed adjacency graph is adopted.
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Lv, Yan, Yaojin Lin, Xiangyan Chen, Dongxing Wang, and Chenxi Wang. "Online Streaming Feature Selection Based on Feature Interaction." In 2020 IEEE International Conference on Knowledge Graph (ICKG). IEEE, 2020. http://dx.doi.org/10.1109/icbk50248.2020.00017.

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Reports on the topic "Feature-based"

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Kramer, Thomas R., John Horst, Hui Huang, Elena Messina, Frederick M. Proctor, and Harry Scott. Feature-based inspection and control system. National Institute of Standards and Technology, 2004. http://dx.doi.org/10.6028/nist.ir.7098.

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Light, Marc. Classification in Feature-Based Default Inheritance Hierarchies. Defense Technical Information Center, 1993. http://dx.doi.org/10.21236/ada281503.

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Vrabel, Michael J. An Approach to Feature-Based Face Recognition. Defense Technical Information Center, 2000. http://dx.doi.org/10.21236/ada390706.

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Kramer, Thomas R., and Frederick M. Proctor. Feature-based control of a machining center. National Institute of Standards and Technology, 1996. http://dx.doi.org/10.6028/nist.ir.5926.

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Thompson, William B., Jonathan C. Owen, and H. J. De St. Germain. Feature-Based Reverse Engineering of Mechanical Parts. Revision. Defense Technical Information Center, 1995. http://dx.doi.org/10.21236/ada437773.

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Stevens, Mark R., and J. R. Beveridge. Optical Linear Feature Detection Based on Model Pose. Defense Technical Information Center, 1995. http://dx.doi.org/10.21236/ada308546.

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Cordeiro de Amorim, Renato. A survey on feature weighting based K-Means algorithms. Web of Open Science, 2020. http://dx.doi.org/10.37686/ser.v1i2.79.

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In a real-world data set there is always the possibility, rather high in our opinion, that different features may have different degrees of relevance. Most machine learning algorithms deal with this fact by either selecting or deselecting features in the data preprocessing phase. However, we maintain that even among relevant features there may be different degrees of relevance, and this should be taken into account during the clustering process. With over 50 years of history, K-Means is arguably the most popular partitional clustering algorithm there is. The first K-Means based clustering algorithm to compute feature weights was designed just over 30 years ago. Various such algorithms have been designed since but there has not been, to our knowledge, a survey integrating empirical evidence of cluster recovery ability, common flaws, and possible directions for future research. This paper elaborates on the concept of feature weighting and addresses these issues by critically analysing some of the most popular, or innovative, feature weighting mechanisms based in K-Means
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Dixon, Kevin. Discriminative feature-rich models for syntax-based machine translation. Office of Scientific and Technical Information (OSTI), 2012. http://dx.doi.org/10.2172/1096485.

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Yu, Lei. Automated Feature Selection for Experience-Based Adaptive Re-planning. Defense Technical Information Center, 2013. http://dx.doi.org/10.21236/ada582125.

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Downs, Christine, Jason Heath, and Teeratorn Kadeethum. Persistent homology-based feature detection from remote-sensing data. Office of Scientific and Technical Information (OSTI), 2022. http://dx.doi.org/10.2172/1887492.

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