Littérature scientifique sur le sujet « Hybrid classifier »
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Articles de revues sur le sujet "Hybrid classifier"
Demidova, Liliya, et Maksim Egin. « Data classification based on the hybrid intellectual technology ». ITM Web of Conferences 18 (2018) : 04001. http://dx.doi.org/10.1051/itmconf/20181804001.
Texte intégralYOUNG KOO, JA, et MYUNGHWAN KIM. « An improved hybrid classifier ». International Journal of Remote Sensing 7, no 3 (mars 1986) : 471–76. http://dx.doi.org/10.1080/01431168608954702.
Texte intégralZhiwen Yu, Le Li, Jiming Liu et Guoqiang Han. « Hybrid Adaptive Classifier Ensemble ». IEEE Transactions on Cybernetics 45, no 2 (février 2015) : 177–90. http://dx.doi.org/10.1109/tcyb.2014.2322195.
Texte intégralSharma, Richa, et Shailendra Narayan Singh. « An Efficient Hybrid Classifier for Prognosing Cardiac Disease ». Webology 19, no 1 (20 janvier 2022) : 5028–46. http://dx.doi.org/10.14704/web/v19i1/web19338.
Texte intégralKotsiantis, Sotiris. « A hybrid decision tree classifier ». Journal of Intelligent & ; Fuzzy Systems 26, no 1 (2014) : 327–36. http://dx.doi.org/10.3233/ifs-120741.
Texte intégralYu, Zhiwen, Hantao Chen, Jiming Liuxs, Jane You, Hareton Leung et Guoqiang Han. « Hybrid $k$ -Nearest Neighbor Classifier ». IEEE Transactions on Cybernetics 46, no 6 (juin 2016) : 1263–75. http://dx.doi.org/10.1109/tcyb.2015.2443857.
Texte intégralDemidova, Liliya A. « Two-Stage Hybrid Data Classifiers Based on SVM and kNN Algorithms ». Symmetry 13, no 4 (7 avril 2021) : 615. http://dx.doi.org/10.3390/sym13040615.
Texte intégralLiu, Su Houn, Hsiu Li Liao, Shih Ming Pi et Chih Chiang Kao. « Patent Classification Using Hybrid Classifier Systems ». Advanced Materials Research 187 (février 2011) : 458–63. http://dx.doi.org/10.4028/www.scientific.net/amr.187.458.
Texte intégralRangel-Díaz-de-la-Vega, Adolfo, Yenny Villuendas-Rey, Cornelio Yáñez-Márquez, Oscar Camacho-Nieto et Itzamá López-Yáñez. « Impact of Imbalanced Datasets Preprocessing in the Performance of Associative Classifiers ». Applied Sciences 10, no 8 (16 avril 2020) : 2779. http://dx.doi.org/10.3390/app10082779.
Texte intégralAnagnostopoulos, Theodoros, et Christos Skourlas. « Ensemble majority voting classifier for speech emotion recognition and prediction ». Journal of Systems and Information Technology 16, no 3 (5 août 2014) : 222–32. http://dx.doi.org/10.1108/jsit-01-2014-0009.
Texte intégralThèses sur le sujet "Hybrid classifier"
Vishnampettai, Sridhar Aadhithya. « A Hybrid Classifier Committee Approach for Microarray Sample Classification ». University of Akron / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=akron1312341058.
Texte intégralNair, Sujit S. « Coarse Radio Signal Classifier on a Hybrid FPGA/DSP/GPP Platform ». Thesis, Virginia Tech, 2009. http://hdl.handle.net/10919/76934.
Texte intégralMaster of Science
Zimit, Sani Ibrahim. « Hybrid approach to interpretable multiple classifier system for intelligent clinical decision support ». Thesis, University of Reading, 2013. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.631699.
Texte intégralLou, Wan Chan. « A hybrid model of tree classifier and neural network for university admission recommender system ». Thesis, University of Macau, 2008. http://umaclib3.umac.mo/record=b1783609.
Texte intégralToubakh, Houari. « Automated on-line early fault diagnosis of wind turbines based on hybrid dynamic classifier ». Thesis, Lille 1, 2015. http://www.theses.fr/2015LIL10100/document.
Texte intégralThis thesis addresses the problem of automatic detection and isolation of drift-like faults in wind turbines (WTs). The main aim of this thesis is to develop a generic on-line adaptive machine learning and data mining scheme that integrates drift detection and isolation mechanism in order to achieve the simple and multiple drift-like fault diagnosis in WTs, in particular pitch system and power converter. The proposed scheme is based on the decomposition of the wind turbine into several components. Then, a classifier is designed and used to achieve the diagnosis of faults impacting each component. The goal of this decomposition into components is to facilitate the isolation of faults and to increase the robustness of the scheme in the sense that when the classifier related to one component is failed, the classifiers for the other components continue to achieve the diagnosis for faults in their corresponding components. This scheme has also the advantage to take into account the WT hybrid dynamics. Indeed, some WT components (as pitch system and power converter) manifest both discrete and continuous dynamic behaviors. In each discrete mode, or a configuration, different continuous dynamics are defined
Rasheed, Sarbast. « A Multiclassifier Approach to Motor Unit Potential Classification for EMG Signal Decomposition ». Thesis, University of Waterloo, 2006. http://hdl.handle.net/10012/934.
Texte intégralThis thesis addresses the process of EMG signal decomposition by developing an interactive classification system, which uses multiple classifier fusion techniques in order to achieve improved classification performance. The developed system combines heterogeneous sets of base classifier ensembles of different kinds and employs either a one level classifier fusion scheme or a hybrid classifier fusion approach.
The hybrid classifier fusion approach is applied as a two-stage combination process that uses a new aggregator module which consists of two combiners: the first at the abstract level of classifier fusion and the other at the measurement level of classifier fusion such that it uses both combiners in a complementary manner. Both combiners may be either data independent or the first combiner data independent and the second data dependent. For the purpose of experimentation, we used as first combiner the majority voting scheme, while we used as the second combiner one of the fixed combination rules behaving as a data independent combiner or the fuzzy integral with the lambda-fuzzy measure as an implicit data dependent combiner.
Once the set of motor unit potential trains are generated by the classifier fusion system, the firing pattern consistency statistics for each train are calculated to detect classification errors in an adaptive fashion. This firing pattern analysis allows the algorithm to modify the threshold of assertion required for assignment of a motor unit potential classification individually for each train based on an expectation of erroneous assignments.
The classifier ensembles consist of a set of different versions of the Certainty classifier, a set of classifiers based on the nearest neighbour decision rule: the fuzzy k-NN and the adaptive fuzzy k-NN classifiers, and a set of classifiers that use a correlation measure as an estimation of the degree of similarity between a pattern and a class template: the matched template filter classifiers and its adaptive counterpart. The base classifiers, besides being of different kinds, utilize different types of features and their performances were investigated using both real and simulated EMG signals of different complexities. The feature sets extracted include time-domain data, first- and second-order discrete derivative data, and wavelet-domain data.
Following the so-called overproduce and choose strategy to classifier ensemble combination, the developed system allows the construction of a large set of candidate base classifiers and then chooses, from the base classifiers pool, subsets of specified number of classifiers to form candidate classifier ensembles. The system then selects the classifier ensemble having the maximum degree of agreement by exploiting a diversity measure for designing classifier teams. The kappa statistic is used as the diversity measure to estimate the level of agreement between the base classifier outputs, i. e. , to measure the degree of decision similarity between the base classifiers. This mechanism of choosing the team's classifiers based on assessing the classifier agreement throughout all the trains and the unassigned category is applied during the one level classifier fusion scheme and the first combiner in the hybrid classifier fusion approach. For the second combiner in the hybrid classifier fusion approach, we choose team classifiers also based on kappa statistics but by assessing the classifiers agreement only across the unassigned category and choose those base classifiers having the minimum agreement.
Performance of the developed classifier fusion system, in both of its variants, i. e. , the one level scheme and the hybrid approach was evaluated using synthetic simulated signals of known properties and real signals and then compared it with the performance of the constituent base classifiers. Across the EMG signal data sets used, the hybrid approach had better average classification performance overall, specially in terms of reducing the number of classification errors.
McCool, Christopher Steven. « Hybrid 2D and 3D face verification ». Thesis, Queensland University of Technology, 2007. https://eprints.qut.edu.au/16436/1/Christopher_McCool_Thesis.pdf.
Texte intégralMcCool, Christopher Steven. « Hybrid 2D and 3D face verification ». Queensland University of Technology, 2007. http://eprints.qut.edu.au/16436/.
Texte intégralAl-Ani, Ahmed Karim. « An improved pattern classification system using optimal feature selection, classifier combination, and subspace mapping techniques ». Thesis, Queensland University of Technology, 2002.
Trouver le texte intégralAla'raj, Maher A. « A credit scoring model based on classifiers consensus system approach ». Thesis, Brunel University, 2016. http://bura.brunel.ac.uk/handle/2438/13669.
Texte intégralLivres sur le sujet "Hybrid classifier"
Wozniak, Michal. Hybrid Classifiers. Berlin, Heidelberg : Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-40997-4.
Texte intégralWozniak, Michal. Hybrid Classifiers : Methods of Data, Knowledge, and Classifier Combination. Springer London, Limited, 2013.
Trouver le texte intégralWozniak, Michal. Hybrid Classifiers : Methods of Data, Knowledge, and Classifier Combination. Springer, 2013.
Trouver le texte intégralWozniak, Michal. Hybrid Classifiers : Methods of Data, Knowledge, and Classifier Combination. Springer Berlin / Heidelberg, 2016.
Trouver le texte intégralFerguson, Ben, et Hillel Steiner. Exploitation. Sous la direction de Serena Olsaretti. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199645121.013.21.
Texte intégralMarcus, Smith, et Leslie Nico. Part I The Nature of Intangible Property, 6 Equity and Debt Securities. Oxford University Press, 2018. http://dx.doi.org/10.1093/law/9780198748434.003.0006.
Texte intégralSmalskys, Vainius, et Jolanta Urbanovič. Civil Service Systems. Oxford University Press, 2017. http://dx.doi.org/10.1093/acrefore/9780190228637.013.160.
Texte intégralChapitres de livres sur le sujet "Hybrid classifier"
Woźniak, Michał. « Classifier Hybridization ». Dans Hybrid Classifiers, 95–140. Berlin, Heidelberg : Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-40997-4_3.
Texte intégralFrédéric, R., et G. Serge. « An Efficient Nearest Neighbor Classifier ». Dans Hybrid Evolutionary Algorithms, 127–45. Berlin, Heidelberg : Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-73297-6_6.
Texte intégralJun, Goo, et Joydeep Ghosh. « Hybrid Hierarchical Classifiers for Hyperspectral Data Analysis ». Dans Multiple Classifier Systems, 42–51. Berlin, Heidelberg : Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02326-2_5.
Texte intégralCohen, Shimon, et Nathan Intrator. « Forward and Backward Selection in Regression Hybrid Network ». Dans Multiple Classifier Systems, 98–107. Berlin, Heidelberg : Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-45428-4_10.
Texte intégralTian, Qi, Jie Yu et Thomas S. Huang. « Boosting Multiple Classifiers Constructed by Hybrid Discriminant Analysis ». Dans Multiple Classifier Systems, 42–52. Berlin, Heidelberg : Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11494683_5.
Texte intégralCohen, Shimon, et Nathan Intrator. « A hybrid projection based and radial basis function architecture ». Dans Multiple Classifier Systems, 147–56. Berlin, Heidelberg : Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-45014-9_14.
Texte intégralCohen, Shimon, et Nathan Intrator. « Automatic Model Selection in a Hybrid Perceptron/Radial Network ». Dans Multiple Classifier Systems, 440–54. Berlin, Heidelberg : Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-48219-9_44.
Texte intégralCohen, Shimon, et Nathan Intrator. « A Study of Ensemble of Hybrid Networks with Strong Regularization ». Dans Multiple Classifier Systems, 227–35. Berlin, Heidelberg : Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-44938-8_23.
Texte intégralKim, Byung Joo. « A Classifier for Big Data ». Dans Convergence and Hybrid Information Technology, 505–12. Berlin, Heidelberg : Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-32692-9_63.
Texte intégralVan, Nguyen Duc, Nguyen Ngoc Doanh, Nguyen Trong Khanh et Nguyen Thi Ngoc Anh. « Hybrid Classifier by Integrating Sentiment and Technical Indicator Classifiers ». Dans Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 25–37. Cham : Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-77818-1_3.
Texte intégralActes de conférences sur le sujet "Hybrid classifier"
Kitanovski, Ivan, Gjorgji Madzarov et Dejan Gjorgjevikj. « Local hybrid SVMDT classifier ». Dans 2011 19th Telecommunications Forum Telfor (TELFOR). IEEE, 2011. http://dx.doi.org/10.1109/telfor.2011.6143658.
Texte intégralBernardini, Flávia Cristina, Ronaldo C. Prati et Maria Carolina Monard. « Evolving Sets of Symbolic Classifiers into a Single Symbolic Classifier Using Genetic Algorithms ». Dans 2008 8th International Conference on Hybrid Intelligent Systems (HIS). IEEE, 2008. http://dx.doi.org/10.1109/his.2008.158.
Texte intégralIqbal, Raja T., et Uvais Qidwai. « Boosted human-centric hybrid classifier ». Dans the 43rd annual southeast regional conference. New York, New York, USA : ACM Press, 2005. http://dx.doi.org/10.1145/1167350.1167373.
Texte intégralGulhane, Yogesh H., et S. A. Ladhake. « Hybrid Approach of Emotion Classifier ». Dans 2018 Second International Conference on Electronics, Communication and Aerospace Technology (ICECA). IEEE, 2018. http://dx.doi.org/10.1109/iceca.2018.8474546.
Texte intégralKabbai, Leila, Mehrez Abdellaoui et Ali Douik. « Hybrid classifier using SIFT descriptor ». Dans 2013 International Conference on Control, Decision and Information Technologies (CoDIT). IEEE, 2013. http://dx.doi.org/10.1109/codit.2013.6689576.
Texte intégralRadha, R., et R. R. Aparna. « Digit Recognition Using Hybrid Classifier ». Dans 2014 World Congress on Computing and Communication Technologies (WCCCT). IEEE, 2014. http://dx.doi.org/10.1109/wccct.2014.18.
Texte intégralYamsaniwar, Sucheta, Surekha Tadse, Saikat Ranajit et Rutvik Walde. « Glaucoma and Cataract Hybrid Classifier ». Dans 2022 10th International Conference on Emerging Trends in Engineering and Technology - Signal and Information Processing (ICETET-SIP-22). IEEE, 2022. http://dx.doi.org/10.1109/icetet-sip-2254415.2022.9791833.
Texte intégralBarbosa, José Matheus Lacerda, Adriano Marabuco de Albuquerque Lima, Paulo Salgado Gomes de Mattos Neto et Adriano Lorena Inácio de Oliveira. « Hybrid Swarm Enhanced Classifier Ensembles ». Dans Encontro Nacional de Inteligência Artificial e Computacional. Sociedade Brasileira de Computação - SBC, 2021. http://dx.doi.org/10.5753/eniac.2021.18263.
Texte intégralLanaridis, A., V. Karakasis et A. Stafylopatis. « Clonal Selection-Based Neural Classifier ». Dans 2008 8th International Conference on Hybrid Intelligent Systems (HIS). IEEE, 2008. http://dx.doi.org/10.1109/his.2008.82.
Texte intégralHartono, Pitoyo, et Shuji Hashimoto. « Ensemble as a Piecewise Linear Classifier ». Dans 2006 Sixth International Conference on Hybrid Intelligent Systems. IEEE, 2006. http://dx.doi.org/10.1109/his.2006.264915.
Texte intégralRapports d'organisations sur le sujet "Hybrid classifier"
Grigsby, Claude C., Ryan M. Kramer, Michael A. Zmuda, Derek W. Boone, Tyler C. Highlander et Mateen M. Rizki. Differential Profiling of Volatile Organic Compound Biomarker Signatures Utilizing a Logical Statistical Filter-Set and Novel Hybrid Evolutionary Classifiers. Fort Belvoir, VA : Defense Technical Information Center, avril 2012. http://dx.doi.org/10.21236/ada562341.
Texte intégralEngel, 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.
Texte intégralDesai, Jairaj, Jijo K. Mathew, Howell Li, Rahul Sakhare, Deborah Horton et Darcy M. Bullock. National Mobility Analysis for All Interstate Routes in the United States. Purdue University, 2023. http://dx.doi.org/10.5703/1288284317585.
Texte intégralKirichek, Galina, Vladyslav Harkusha, Artur Timenko et Nataliia Kulykovska. System for detecting network anomalies using a hybrid of an uncontrolled and controlled neural network. [б. в.], février 2020. http://dx.doi.org/10.31812/123456789/3743.
Texte intégralDesai, Jairaj, Jijo K. Mathew, Howell Li, Rahul Suryakant Sakhare, Deborah Horton et Darcy M. Bullock. National Mobility Report for All Interstates–December 2022. Purdue University, 2023. http://dx.doi.org/10.5703/1288284317591.
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