Littérature scientifique sur le sujet « FEATURE OPTIMIZATION METHODS »
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Articles de revues sur le sujet "FEATURE OPTIMIZATION METHODS"
Hamad, Zana O. « REVIEW OF FEATURE SELECTION METHODS USING OPTIMIZATION ALGORITHM ». Polytechnic Journal 12, no 2 (15 mars 2023) : 203–14. http://dx.doi.org/10.25156/ptj.v12n2y2022.pp203-214.
Texte intégralZhang, Yang, Emil Tochev, Svetan Ratchev et Carl German. « Production process optimization using feature selection methods ». Procedia CIRP 88 (2020) : 554–59. http://dx.doi.org/10.1016/j.procir.2020.05.096.
Texte intégralGoodarzi, Mohammad, Bieke Dejaegher et Yvan Vander Heyden. « Feature Selection Methods in QSAR Studies ». Journal of AOAC INTERNATIONAL 95, no 3 (1 mai 2012) : 636–51. http://dx.doi.org/10.5740/jaoacint.sge_goodarzi.
Texte intégralJameel, Noor, et Hasanen S. Abdullah. « Intelligent Feature Selection Methods : A Survey ». Engineering and Technology Journal 39, no 1B (25 mars 2021) : 175–83. http://dx.doi.org/10.30684/etj.v39i1b.1623.
Texte intégralWu, Shaohua, Yong Hu, Wei Wang, Xinyong Feng et Wanneng Shu. « Application of Global Optimization Methods for Feature Selection and Machine Learning ». Mathematical Problems in Engineering 2013 (2013) : 1–8. http://dx.doi.org/10.1155/2013/241517.
Texte intégralWein, Fabian, Peter D. Dunning et Julián A. Norato. « A review on feature-mapping methods for structural optimization ». Structural and Multidisciplinary Optimization 62, no 4 (3 août 2020) : 1597–638. http://dx.doi.org/10.1007/s00158-020-02649-6.
Texte intégralLarabi-Marie-Sainte, Souad. « Outlier Detection Based Feature Selection Exploiting Bio-Inspired Optimization Algorithms ». Applied Sciences 11, no 15 (23 juillet 2021) : 6769. http://dx.doi.org/10.3390/app11156769.
Texte intégralBoubezoul, Abderrahmane, et Sébastien Paris. « Application of global optimization methods to model and feature selection ». Pattern Recognition 45, no 10 (octobre 2012) : 3676–86. http://dx.doi.org/10.1016/j.patcog.2012.04.015.
Texte intégralUzun, Mehmet Zahit, Yuksel Celik et Erdal Basaran. « Micro-Expression Recognition by Using CNN Features with PSO Algorithm and SVM Methods ». Traitement du Signal 39, no 5 (30 novembre 2022) : 1685–93. http://dx.doi.org/10.18280/ts.390526.
Texte intégralLiu, Yong Xia, Ru Shu Peng, Ai Hong Hou et De Wen Tang. « Methods of Cam Structure Optimization Based on Behavioral Modeling ». Advanced Materials Research 139-141 (octobre 2010) : 1245–48. http://dx.doi.org/10.4028/www.scientific.net/amr.139-141.1245.
Texte intégralThèses sur le sujet "FEATURE OPTIMIZATION METHODS"
Lin, Lei. « Optimization methods for inventive design ». Thesis, Strasbourg, 2016. http://www.theses.fr/2016STRAD012/document.
Texte intégralThe thesis deals with problems of invention where solutions optimization methods do not meet the objectives of problems to solve. The problems previuosly defined exploit for their resolution, a problem extending the model of classical TRIZ in a canonical form called "generalized system of contradictions." This research draws up a resolution process based on the loop simulation-optimization-invention using both solving methods of optimization and invention. More precisely, it models the extraction of generalized contractions from simulation data as combinatorial optimization problems and offers algorithms that offer all the solutions to these problems
Zanco, Philip. « Analysis of Optimization Methods in Multisteerable Filter Design ». ScholarWorks@UNO, 2016. http://scholarworks.uno.edu/td/2227.
Texte intégralMonrousseau, Thomas. « Développement du système d'analyse des données recueillies par les capteurs et choix du groupement de capteurs optimal pour le suivi de la cuisson des aliments dans un four ». Thesis, Toulouse, INSA, 2016. http://www.theses.fr/2016ISAT0054.
Texte intégralIn a world where all personal devices become smart and connected, some French industrials created a project to make ovens able detecting the cooking state of fish and meat without contact sensor. This thesis takes place in this context and is divided in two major parts. The first one is a feature selection phase to be able to classify food in three states: under baked, well baked and over baked. The point of this selection method, based on fuzzy logic is to strongly reduce the number of features got from laboratory specific sensors. The second part concerns on-line monitoring of the food cooking state by several methods. These technics are: classification algorithm into ten bake states, the use of a discrete version of the heat equation and the development of a soft sensor based on an artificial neural network model build from cooking experiments to infer the temperature inside the food from available on-line measurements. These algorithms have been implemented on microcontroller equipping a prototype version of a new oven in order to be tested and validated on real use cases
Xiong, Xuehan. « Supervised Descent Method ». Research Showcase @ CMU, 2015. http://repository.cmu.edu/dissertations/652.
Texte intégralLösch, Felix. « Optimization of variability in software product lines a semi-automatic method for visualization, analysis, and restructuring of variability in software product lines ». Berlin Logos-Verl, 2008. http://d-nb.info/992075904/04.
Texte intégralBai, Bing. « A Study of Adaptive Random Features Models in Machine Learning based on Metropolis Sampling ». Thesis, KTH, Numerisk analys, NA, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-293323.
Texte intégralI artificiella neurala nätverk (ANN), som används inom maskininlärning, behöver parametrar, kallade frekvensparametrar och amplitudparametrar, hittasgenom en så kallad träningsprocess. Random feature-modeller är ett specialfall av ANN där träningen sker på ett annat sätt. I dessa modeller tränasamplitudparametrarna medan frekvensparametrarna samplas från någon sannolikhetstäthet. Om denna sannolikhetstäthet valts med omsorg kommer båda träningsmodellerna att ge god approximation av givna data. Metoden Adaptiv random Fourier feature[1] uppdaterar frekvensfördelningen adaptivt. Denna uppsats studerar aktiveringsfunktionerna ReLU och sigmoid och kombinerar dem med den adaptiva iden i [1] för att generera två ytterligare Random feature-modeller. Resultaten visar att om samma hyperparametrar som i [1] används så kan den adaptiva ReLU features-modellen approximera data relativt väl, även om Fourier features-modellen ger något bättre resultat.
Sasse, Hugh Granville. « Enhancing numerical modelling efficiency for electromagnetic simulation of physical layer components ». Thesis, De Montfort University, 2010. http://hdl.handle.net/2086/4406.
Texte intégralYADAV, JYOTI. « A STUDY OF FEATURE OPTIMIZATION METHODS FOR LUNG CANCER DETECTION ». Thesis, 2022. http://dspace.dtu.ac.in:8080/jspui/handle/repository/19156.
Texte intégralSalehipour, Amir. « Combinatorial optimization methods for the (alpha,beta)-k Feature Set Problem ». Thesis, 2019. http://hdl.handle.net/1959.13/1400399.
Texte intégralThis PhD research thesis proposes novel and efficient combinatorial optimization-based solution methods for the (alpha,beta)-k Feature Set Problem. The (alpha,beta)-k Feature Set Problem is a combinatorial optimization-based feature selection approach proposed in 2004, and has several applications in computational biology and Bioinformatics. The (alpha,beta)-k Feature Set Problem aims to select a minimum cost set of features such that similarities between entities of the same class and differences between entities of different classes are maximized. The developed solution methods of this research include heuristic and exact methods. While this research focuses on utilizing exact methods, we also developed mathematical properties, and heuristics and problem-driven local searches and applied them in certain stages of the exact methods in order to guide exact solvers and deliver high quality solutions. The motivation behind this stems from computational difficulty of exact solvers in providing good quality solutions for the (alpha, beta)-k Feature Set Problem. Our proposed heuristics deliver very good quality solutions including optimal, and that in a reasonable amount of time. The major contributions of the presented research include: 1) investigating and exploring mathematical properties and characteristics of the (alpha,beta)-k Feature Set Problem for the first time, and utilizing those in order to design and develop algorithms and methods for solving large instances of the (alpha,beta)-k Feature Set Problem; 2) extending the basic modeling, algorithms and solution methods to the weighted variant of the (alpha,beta)-k Feature Set Problem (where features have a cost); and, 3) developing algorithms and solution methods that are capable of solving large instances of the (alpha,beta)-k Feature Set Problem in a reasonable amount of time (prior to this research, many of those instances pose a computational challenge for the exact solvers). To this end, we showed the usefulness of the developed algorithms and methods by applying them on three sets of 346 instances, including real-world, weighted, and randomly generated instances, and obtaining high quality solutions in a short time. To the best of our knowledge, the developed algorithms of this research have obtained the best results for the (alpha,beta)-k Feature Set Problem. In particular, they outperform state-of-the-art algorithms and exact solvers, and have a very competitive performance over large instances because they always deliver feasible solutions, and obtain new best solutions for a majority of large instances in a reasonable amount of time.
Tayal, Aditya. « Effective and Efficient Optimization Methods for Kernel Based Classification Problems ». Thesis, 2014. http://hdl.handle.net/10012/8334.
Texte intégralLivres sur le sujet "FEATURE OPTIMIZATION METHODS"
The Feature-Driven Method for Structural Optimization. Elsevier, 2021. http://dx.doi.org/10.1016/c2019-0-03253-0.
Texte intégralFeature-Driven Method for Structural Optimization Design. Elsevier, 2020.
Trouver le texte intégralZhou, Ying, et Weihong Zhang. Feature-Driven Method for Structural Optimization Design. Elsevier, 2021.
Trouver le texte intégralHilgurt, S. Ya, et O. A. Chemerys. Reconfigurable signature-based information security tools of computer systems. PH “Akademperiodyka”, 2022. http://dx.doi.org/10.15407/akademperiodyka.458.297.
Texte intégralZaheer Ul-Haq et Angela K. Wilson, dir. Frontiers in Computational Chemistry : Volume 6. BENTHAM SCIENCE PUBLISHERS, 2022. http://dx.doi.org/10.2174/97898150368481220601.
Texte intégralBacior, Stanisław. Optymalizacja wiejskich układów gruntowych – badania eksperymentalne. Publishing House of the University of Agriculture in Krakow, 2019. http://dx.doi.org/10.15576/978-83-66602-37-3.
Texte intégralRailsback, Steven F., et Bret C. Harvey. Modeling Populations of Adaptive Individuals. Princeton University Press, 2020. http://dx.doi.org/10.23943/princeton/9780691195285.001.0001.
Texte intégralMehta, Vaishali, Dolly Sharma, Monika Mangla, Anita Gehlot, Rajesh Singh et Sergio Márquez Sánchez, dir. Challenges and Opportunities for Deep Learning Applications in Industry 4.0. BENTHAM SCIENCE PUBLISHERS, 2022. http://dx.doi.org/10.2174/97898150360601220101.
Texte intégralUfimtseva, Nataliya V., Iosif A. Sternin et Elena Yu Myagkova. Russian psycholinguistics : results and prospects (1966–2021) : a research monograph. Institute of Linguistics, Russian Academy of Sciences, 2021. http://dx.doi.org/10.30982/978-5-6045633-7-3.
Texte intégralChapitres de livres sur le sujet "FEATURE OPTIMIZATION METHODS"
Balavand, Alireza, et Soheyla Pahlevani. « Proposing a New Feature Clustering Method in Order to the Binary Classification of COVID-19 in Computed Tomography Images ». Dans Engineering Optimization : Methods and Applications, 193–216. Singapore : Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-1521-7_11.
Texte intégralMathieson, Luke, Alexandre Mendes, John Marsden, Jeffrey Pond et Pablo Moscato. « Computer-Aided Breast Cancer Diagnosis with Optimal Feature Sets : Reduction Rules and Optimization Techniques ». Dans Methods in Molecular Biology, 299–325. New York, NY : Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4939-6613-4_17.
Texte intégralBürger, Fabian, et Josef Pauli. « A Holistic Classification Optimization Framework with Feature Selection, Preprocessing, Manifold Learning and Classifiers ». Dans Pattern Recognition : Applications and Methods, 52–68. Cham : Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-27677-9_4.
Texte intégralUtracki, Jarosław, et Mariusz Boryczka. « Evolutionary and Aggressive Sampling for Pattern Revelation and Precognition in Building Energy Managing System with Nature-Based Methods for Energy Optimization ». Dans Advances in Feature Selection for Data and Pattern Recognition, 295–319. Cham : Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67588-6_15.
Texte intégralSchneider, Lennart, Lennart Schäpermeier, Raphael Patrick Prager, Bernd Bischl, Heike Trautmann et Pascal Kerschke. « HPO $$\times $$ ELA : Investigating Hyperparameter Optimization Landscapes by Means of Exploratory Landscape Analysis ». Dans Lecture Notes in Computer Science, 575–89. Cham : Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-14714-2_40.
Texte intégralGarcia, Rodolfo, Emerson Cabrera Paraiso et Júlio Cesar Nievola. « Multiobjective Optimization of Indexes Obtained by Clustering for Feature Selection Methods Evaluation in Genes Expression Microarrays ». Dans Lecture Notes in Computer Science, 353–60. Berlin, Heidelberg : Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23878-9_42.
Texte intégralWang, Jingbo, Yannan Li et Chao Wang. « Synthesizing Fair Decision Trees via Iterative Constraint Solving ». Dans Computer Aided Verification, 364–85. Cham : Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-13188-2_18.
Texte intégralFigueira, Jose Rui, Salvatore Greco, Bernard Roy et Roman Słowiński. « ELECTRE Methods : Main Features and Recent Developments ». Dans Applied Optimization, 51–89. Berlin, Heidelberg : Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-540-92828-7_3.
Texte intégralHernández, A. I., J. Dumont, M. Altuve, A. Beuchée et G. Carrault. « Evolutionary Optimization of ECG Feature Extraction Methods : Applications to the Monitoring of Adult Myocardial Ischemia and Neonatal Apnea Bradycardia Events ». Dans ECG Signal Processing, Classification and Interpretation, 237–73. London : Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-868-3_11.
Texte intégralCheng, G., S. Meng, S. Liu, Y. Jiao, X. Chen, W. Zhang, H. Wen, W. Zhang, B. Wang et X. Xu. « An Exploration into the Optimization of Feature Wavelength Screening Methods in the Processing of Frozen Fish Classification Data in Near Infrared Spectroscopy ». Dans Sense the Real Change : Proceedings of the 20th International Conference on Near Infrared Spectroscopy, 97–107. Singapore : Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-4884-8_9.
Texte intégralActes de conférences sur le sujet "FEATURE OPTIMIZATION METHODS"
Talab, Mohammed Ahmed, Neven Ali Qahraman, Mais Muneam Aftan, Alaa Hamid Mohammed et Mohd Dilshad Ansari. « Local Feature Methods Based Facial Recognition ». Dans 2022 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA). IEEE, 2022. http://dx.doi.org/10.1109/hora55278.2022.9799910.
Texte intégralAgarwal, Dheeraj, Simao Marques, Trevor T. Robinson, Cecil G. Armstrong et Philip Hewitt. « Aerodynamic Shape Optimization Using Feature based CAD Systems and Adjoint Methods ». Dans 18th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference. Reston, Virginia : American Institute of Aeronautics and Astronautics, 2017. http://dx.doi.org/10.2514/6.2017-3999.
Texte intégralProchazka, Michal, Zuzana Oplatkova, Jiri Holoska et Vladimir Gerlich. « Optimization Of Neural Network Inputs By Feature Selection Methods ». Dans 25th Conference on Modelling and Simulation. ECMS, 2011. http://dx.doi.org/10.7148/2011-0440-0445.
Texte intégralHussain, Chesti Altaff, D. Venkata Rao et S. Aruna Mastani. « Low level feature extraction methods for Content Based Image Retrieval ». Dans 2015 International Conference on Electrical, Electronics, Signals, Communication and Optimization (EESCO). IEEE, 2015. http://dx.doi.org/10.1109/eesco.2015.7253924.
Texte intégralZhang, Jia, Yidong Lin, Min Jiang, Shaozi Li, Yong Tang et Kay Chen Tan. « Multi-label Feature Selection via Global Relevance and Redundancy Optimization ». Dans Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California : International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/348.
Texte intégralGriparis, Andreea, Daniela Faur et Mihai Datcu. « Feature space dimensionality reduction for the optimization of visualization methods ». Dans IGARSS 2015 - 2015 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2015. http://dx.doi.org/10.1109/igarss.2015.7325967.
Texte intégralBürger, Fabian, et Josef Pauli. « Representation Optimization with Feature Selection and Manifold Learning in a Holistic Classification Framework ». Dans International Conference on Pattern Recognition Applications and Methods. SCITEPRESS - Science and and Technology Publications, 2015. http://dx.doi.org/10.5220/0005183600350044.
Texte intégralÖzseven, Turgut, et Mustafa Arpacioglu. « Classification of Urban Sounds with PSO and WO Based Feature Selection Methods ». Dans 2023 5th International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA). IEEE, 2023. http://dx.doi.org/10.1109/hora58378.2023.10156803.
Texte intégralKhurma, Ruba, Ibrahim Aljarah et Ahmad Sharieh. « An Efficient Moth Flame Optimization Algorithm using Chaotic Maps for Feature Selection in the Medical Applications ». Dans 9th International Conference on Pattern Recognition Applications and Methods. SCITEPRESS - Science and Technology Publications, 2020. http://dx.doi.org/10.5220/0008960701750182.
Texte intégralWeber Martins, Thiago, et Reiner Anderl. « Feature Recognition and Parameterization Methods for Algorithm-Based Product Development Process ». Dans ASME 2017 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/detc2017-67031.
Texte intégralRapports d'organisations sur le sujet "FEATURE OPTIMIZATION METHODS"
Engel, Bernard, Yael Edan, James Simon, Hanoch Pasternak et Shimon Edelman. Neural Networks for Quality Sorting of Agricultural Produce. United States Department of Agriculture, juillet 1996. http://dx.doi.org/10.32747/1996.7613033.bard.
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