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Готові списки джерел за темами / Hybrid classifier

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Добірка наукової літератури з теми "Hybrid classifier"

Автор: Grafiati

Опубліковано: 11 березня 2023

Оновлено: 26 липня 2025

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Статті в журналах з теми "Hybrid classifier"

1

Demidova, Liliya, and 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.

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In this paper the data classification technique, implying the consistent application of the SVM and Parzen classifiers, has been suggested. The Parser classifier applies to data which can be both correctly and erroneously classified using the SVM classifier, and are located in the experimentally defined subareas near the hyperplane which separates the classes. A herewith, the SVM classifier is used with the default parameters values, and the optimal parameters values of the Parser classifier are determined using the genetic algorithm. The experimental results confirming the effectiveness of th

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2

YOUNG KOO, JA, and MYUNGHWAN KIM. "An improved hybrid classifier." International Journal of Remote Sensing 7, no. 3 (1986): 471–76. http://dx.doi.org/10.1080/01431168608954702.

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3

Zhiwen Yu, Le Li, Jiming Liu, and Guoqiang Han. "Hybrid Adaptive Classifier Ensemble." IEEE Transactions on Cybernetics 45, no. 2 (2015): 177–90. http://dx.doi.org/10.1109/tcyb.2014.2322195.

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4

Sharma, Richa, and Shailendra Narayan Singh. "An Efficient Hybrid Classifier for Prognosing Cardiac Disease." Webology 19, no. 1 (2022): 5028–46. http://dx.doi.org/10.14704/web/v19i1/web19338.

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Анотація:

Machine learning (ML) is a powerful tool which empowers the practitioners for predictions upon any existing or real- time data. Here, the Machine first understands the valuable patterns from the dataset and then uses that information to make predictions on the unknown data. Further, classification is the commonly used machine learning approach (ML-Approach) to make such predictions. The objective of this work aims to design and development of an ensemble classifier for prognosing cardiovascular disease (heart disease). The developed classifier integrates Support Vector Machine (SVM), K–Nearest

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5

Demidova, Liliya A. "Two-Stage Hybrid Data Classifiers Based on SVM and kNN Algorithms." Symmetry 13, no. 4 (2021): 615. http://dx.doi.org/10.3390/sym13040615.

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The paper considers a solution to the problem of developing two-stage hybrid SVM-kNN classifiers with the aim to increase the data classification quality by refining the classification decisions near the class boundary defined by the SVM classifier. In the first stage, the SVM classifier with default parameters values is developed. Here, the training dataset is designed on the basis of the initial dataset. When developing the SVM classifier, a binary SVM algorithm or one-class SVM algorithm is used. Based on the results of the training of the SVM classifier, two variants of the training datase

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6

Ramamoorthy, Karthikamani, and Harikumar Rajaguru. "Exploitation of Bio-Inspired Classifiers for Performance Enhancement in Liver Cirrhosis Detection from Ultrasonic Images." Biomimetics 9, no. 6 (2024): 356. http://dx.doi.org/10.3390/biomimetics9060356.

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In the current scenario, liver abnormalities are one of the most serious public health concerns. Cirrhosis of the liver is one of the foremost causes of demise from liver diseases. To accurately predict the status of liver cirrhosis, physicians frequently use automated computer-aided approaches. In this paper, through clustering techniques like fuzzy c-means (FCM), possibilistic fuzzy c-means (PFCM), and possibilistic c means (PCM) and sample entropy features are extracted from normal and cirrhotic liver ultrasonic images. The extracted features are classified as normal and cirrhotic through t

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Kotsiantis, 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.

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8

Yu, Zhiwen, Hantao Chen, Jiming Liuxs, Jane You, Hareton Leung, and Guoqiang Han. "Hybrid $k$ -Nearest Neighbor Classifier." IEEE Transactions on Cybernetics 46, no. 6 (2016): 1263–75. http://dx.doi.org/10.1109/tcyb.2015.2443857.

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Janane, S. K. 1. Keerthana M. S. 1. Subbulakshmi B. *1. "HYBRID CLASSIFICATION FOR SENTIMENT ANALYSIS OF MOVIE REVIEWS." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 7, no. 4 (2018): 724–28. https://doi.org/10.5281/zenodo.1228816.

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Internet has provided people a platform to express their opinions and thoughts. Sentiment analysis helps to analyse those opinions and categorize them. This research is done on the movie review dataset obtained from the Internet Movie Database (IMDb). The data is classified using some of the popular learning based classifiers like Naive Bayes, Decision Tree and Support Vector Machine (SVM) classifiers and their accuracies are compared. Finally, the three learning based classifiers are combined using the Majority vote ensemble classifier. It is found that the accuracy obtained from the above sa

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Rangel-Díaz-de-la-Vega, Adolfo, Yenny Villuendas-Rey, Cornelio Yáñez-Márquez, Oscar Camacho-Nieto, and Itzamá López-Yáñez. "Impact of Imbalanced Datasets Preprocessing in the Performance of Associative Classifiers." Applied Sciences 10, no. 8 (2020): 2779. http://dx.doi.org/10.3390/app10082779.

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In this paper, an experimental study was carried out to determine the influence of imbalanced datasets preprocessing in the performance of associative classifiers, in order to find the better computational solutions to the problem of credit scoring. To do this, six undersampling algorithms, six oversampling algorithms and four hybrid algorithms were evaluated in 13 imbalanced datasets referring to credit scoring. Then, the performance of four associative classifiers was analyzed. The experiments carried out allowed us to determine which sampling algorithms had the best results, as well as thei

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Дисертації з теми "Hybrid classifier"

1

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.

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2

Nair, Sujit S. "Coarse Radio Signal Classifier on a Hybrid FPGA/DSP/GPP Platform." Thesis, Virginia Tech, 2009. http://hdl.handle.net/10919/76934.

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The Virginia Tech Universal Classifier Synchronizer (UCS) system can enable a cognitive receiver to detect, classify and extract all the parameters needed from a received signal for physical layer demodulation and configure a cognitive radio accordingly. Currently, UCS can process analog amplitude modulation (AM) and frequency modulation (FM) and digital narrow band M-PSK, M-QAM and wideband signal orthogonal frequency division multiplexing (OFDM). A fully developed prototype of UCS system was designed and implemented in our laboratory using GNU radio software platform and Universal Software R

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3

Sayin, Günel Burcu. "Towards Reliable Hybrid Human-Machine Classifiers." Doctoral thesis, Università degli studi di Trento, 2022. http://hdl.handle.net/11572/349843.

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In this thesis, we focus on building reliable hybrid human-machine classifiers to be deployed in cost-sensitive classification tasks. The objective is to assess ML quality in hybrid classification contexts and design the appropriate metrics, thereby knowing whether we can trust the model predictions and identifying the subset of items on which the model is well-calibrated and trustworthy. We start by discussing the key concepts, research questions, challenges, and architecture to design and implement an effective hybrid classification service. We then present a deeper investigation of each ser

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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.

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Data-driven decision support approaches have been increasingly employed in recent years in order to unveil useful diagnostic and prognostic patterns from data accumulated in clinical repositories. Given the diverse amount of evidence generated through everyday clinical practice and the exponential growth in the number of parameters accumulated in the data, the capability of finding purposeful task-oriented patterns from patient records is crucial for providing effective healthcare delivery. The application of classification decision support tool in clinical settings has brought about formidabl

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Lou, 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.

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6

Toubakh, 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.

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L'objectif principal de cette thèse est de développer un schéma générique et adaptatif basée sur les approches d'apprentissage automatique, intégrant des mécanismes de détection et d'isolation des défauts avec une force d’apparition progressive. Le but de ce schéma est de réaliser le diagnostic en ligne des défauts simple et multiple de type dérive dans les systèmes éoliens, et plus particulièrement dans le système du calage des pales et le convertisseur de puissance. Le schéma proposé est basé sur la décomposition du système éolien en plusieurs composantes. Ensuite, un classifieur est conçu e

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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.

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EMG signal decomposition is the process of resolving a composite EMG signal into its constituent motor unit potential trains (classes) and it can be configured as a classification problem. An EMG signal detected by the tip of an inserted needle electrode is the superposition of the individual electrical contributions of the different motor units that are active, during a muscle contraction, and background interference. <BR>This thesis addresses the process of EMG signal decomposition by developing an interactive classification system, which uses multiple classifier fusion techniqu

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8

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.

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Face verification is a challenging pattern recognition problem. The face is a biometric that, we as humans, know can be recognised. However, the face is highly deformable and its appearance alters significantly when the pose, illumination or expression changes. These changes in appearance are most notable for texture images, or two-dimensional (2D) data. But the underlying structure of the face, or three dimensional (3D) data, is not changed by pose or illumination variations. Over the past five years methods have been investigated to combine 2D and 3D face data to improve the acc

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McCool, Christopher Steven. "Hybrid 2D and 3D face verification." Queensland University of Technology, 2007. http://eprints.qut.edu.au/16436/.

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Анотація:

Face verification is a challenging pattern recognition problem. The face is a biometric that, we as humans, know can be recognised. However, the face is highly deformable and its appearance alters significantly when the pose, illumination or expression changes. These changes in appearance are most notable for texture images, or two-dimensional (2D) data. But the underlying structure of the face, or three dimensional (3D) data, is not changed by pose or illumination variations. Over the past five years methods have been investigated to combine 2D and 3D face data to improve the accuracy an

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10

Al-Ani, Ahmed Karim. "An improved pattern classification system using optimal feature selection, classifier combination, and subspace mapping techniques." Thesis, Queensland University of Technology, 2002.

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Книги з теми "Hybrid classifier"

1

Wozniak, Michal. Hybrid Classifiers. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-40997-4.

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Wozniak, Michal. Hybrid Classifiers: Methods of Data, Knowledge, and Classifier Combination. Springer Berlin / Heidelberg, 2016.

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3

Wozniak, Michal. Hybrid Classifiers: Methods of Data, Knowledge, and Classifier Combination. Springer London, Limited, 2013.

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4

Wozniak, Michal. Hybrid Classifiers: Methods of Data, Knowledge, and Classifier Combination. Springer, 2013.

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5

Ferguson, Ben, and Hillel Steiner. Exploitation. Edited by Serena Olsaretti. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199645121.013.21.

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Exploitation is commonly understood as taking unfair advantage. This article discusses the various prominent accounts that have been offered of how an exchange, despite being Pareto improving and consensual, can nevertheless count as unfair or unjust and, hence, as presumptively impermissible. Does the wrongness of an exploitative transaction consist in its compounding a prior distributive injustice, or in its deliberately profiting from someone’s vulnerability, or in its commodification of that which should not be commodified? How should responsibility for exploitation be assigned, and can th

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6

Marcus, Smith, and 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.

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Анотація:

This chapter discusses securities. Securities are an important and complex category of intangible. A ‘security’ is defined as a fungible financial instrument, offered for sale on identical terms to multiple investors on first issue, and thereafter generally traded in a market that facilitates its free transfer. Securities can broadly be classified into shares, debt securities, and hybrid securities. The chapter then looks at the legal incidents of securities, and how securities are allotted and held. Allotment describes the process whereby the issuer of securities agrees to issue those securit

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Pullen, Jennifer, Sean Prentiss, Amorak Huey, et al. Fantasy Fiction. Bloomsbury Publishing Plc, 2023. http://dx.doi.org/10.5040/9781350166967.

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Анотація:

The first fantasy-writing textbook to combine a historical genre overview with an anthology and comprehensive craft guide, this book explores the blue prints of one of the most popular forms of genre fiction. The first section will acquaint readers with the vast canon of existing fantasy fiction and outline the many sub-genres encompassed within it before examining the important relationship between fantasy and creative writing, the academy and publishing. A craft guide follows which equips students with the key concepts of storytelling as they are impacted by writing through a fantastical len

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Smalskys, Vainius, and Jolanta Urbanovič. Civil Service Systems. Oxford University Press, 2017. http://dx.doi.org/10.1093/acrefore/9780190228637.013.160.

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Civil service consists of civil servants and their activity when implementing the assigned functions and decisions made by politicians. In other words, it is a system of civil servants who perform the assigned functions of public administration. The corpus of civil servants consists of people who work in central and local public administration institutions. The concept and scope of civil service in a particular country depends on the legal framework that defines the areas of public and private sectors and their relationship. In many countries, civil service consists of an upper level, a mid-le

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Частини книг з теми "Hybrid classifier"

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Woźniak, Michał. "Classifier Hybridization." In Hybrid Classifiers. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-40997-4_3.

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Frédéric, R., and G. Serge. "An Efficient Nearest Neighbor Classifier." In Hybrid Evolutionary Algorithms. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-73297-6_6.

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3

Jun, Goo, and Joydeep Ghosh. "Hybrid Hierarchical Classifiers for Hyperspectral Data Analysis." In Multiple Classifier Systems. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02326-2_5.

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4

Cohen, Shimon, and Nathan Intrator. "Forward and Backward Selection in Regression Hybrid Network." In Multiple Classifier Systems. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-45428-4_10.

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Tian, Qi, Jie Yu, and Thomas S. Huang. "Boosting Multiple Classifiers Constructed by Hybrid Discriminant Analysis." In Multiple Classifier Systems. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11494683_5.

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Cohen, Shimon, and Nathan Intrator. "A hybrid projection based and radial basis function architecture." In Multiple Classifier Systems. Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-45014-9_14.

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Cohen, Shimon, and Nathan Intrator. "Automatic Model Selection in a Hybrid Perceptron/Radial Network." In Multiple Classifier Systems. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-48219-9_44.

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Cohen, Shimon, and Nathan Intrator. "A Study of Ensemble of Hybrid Networks with Strong Regularization." In Multiple Classifier Systems. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-44938-8_23.

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Kim, Byung Joo. "A Classifier for Big Data." In Convergence and Hybrid Information Technology. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-32692-9_63.

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Van, Nguyen Duc, Nguyen Ngoc Doanh, Nguyen Trong Khanh, and Nguyen Thi Ngoc Anh. "Hybrid Classifier by Integrating Sentiment and Technical Indicator Classifiers." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-77818-1_3.

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Тези доповідей конференцій з теми "Hybrid classifier"

1

Nagamani, T., and Usha Rani Nelakuditi. "Multi-Crop Multi-Disease Classifier Using Hybrid Model." In 2024 IEEE International Conference on Future Machine Learning and Data Science (FMLDS). IEEE, 2024. https://doi.org/10.1109/fmlds63805.2024.00094.

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Kitanovski, Ivan, Gjorgji Madzarov, and Dejan Gjorgjevikj. "Local hybrid SVMDT classifier." In 2011 19th Telecommunications Forum Telfor (TELFOR). IEEE, 2011. http://dx.doi.org/10.1109/telfor.2011.6143658.

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Bernardini, Flávia Cristina, Ronaldo C. Prati, and Maria Carolina Monard. "Evolving Sets of Symbolic Classifiers into a Single Symbolic Classifier Using Genetic Algorithms." In 2008 8th International Conference on Hybrid Intelligent Systems (HIS). IEEE, 2008. http://dx.doi.org/10.1109/his.2008.158.

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Lanaridis, A., V. Karakasis, and A. Stafylopatis. "Clonal Selection-Based Neural Classifier." In 2008 8th International Conference on Hybrid Intelligent Systems (HIS). IEEE, 2008. http://dx.doi.org/10.1109/his.2008.82.

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Iqbal, Raja T., and Uvais Qidwai. "Boosted human-centric hybrid classifier." In the 43rd annual southeast regional conference. ACM Press, 2005. http://dx.doi.org/10.1145/1167350.1167373.

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Gulhane, Yogesh H., and S. A. Ladhake. "Hybrid Approach of Emotion Classifier." In 2018 Second International Conference on Electronics, Communication and Aerospace Technology (ICECA). IEEE, 2018. http://dx.doi.org/10.1109/iceca.2018.8474546.

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Kabbai, Leila, Mehrez Abdellaoui, and Ali Douik. "Hybrid classifier using SIFT descriptor." In 2013 International Conference on Control, Decision and Information Technologies (CoDIT). IEEE, 2013. http://dx.doi.org/10.1109/codit.2013.6689576.

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Radha, R., and R. R. Aparna. "Digit Recognition Using Hybrid Classifier." In 2014 World Congress on Computing and Communication Technologies (WCCCT). IEEE, 2014. http://dx.doi.org/10.1109/wccct.2014.18.

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Yamsaniwar, Sucheta, Surekha Tadse, Saikat Ranajit, and Rutvik Walde. "Glaucoma and Cataract Hybrid Classifier." In 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.

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Barbosa, José Matheus Lacerda, Adriano Marabuco de Albuquerque Lima, Paulo Salgado Gomes de Mattos Neto, and Adriano Lorena Inácio de Oliveira. "Hybrid Swarm Enhanced Classifier Ensembles." In Encontro Nacional de Inteligência Artificial e Computacional. Sociedade Brasileira de Computação - SBC, 2021. http://dx.doi.org/10.5753/eniac.2021.18263.

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Анотація:

Os Sistemas de Multi-Classificadores (MCSs) constituem um dos paradigmas mais competitivos para a obtenção de classificações precisas no campo do aprendizado de máquina. Este artigo busca avaliar se a utilização de algoritmos híbridos de enxames pode melhorar a performance dos MCSs por meio da otimização de pesos em combinações por voto majoritário ponderado. A metodologia proposta rendeu resultados competitivos em 25 conjuntos de dados de referência. Adotou-se a acurácia como função objetivo a ser maximizada pelas seguintes meta-heurísticas: otimização do exame de partículas (PSO), a colônia

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Звіти організацій з теми "Hybrid classifier"

1

Engel, Bernard, Yael Edan, James Simon, Hanoch Pasternak, and Shimon Edelman. Neural Networks for Quality Sorting of Agricultural Produce. United States Department of Agriculture, 1996. http://dx.doi.org/10.32747/1996.7613033.bard.

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The objectives of this project were to develop procedures and models, based on neural networks, for quality sorting of agricultural produce. Two research teams, one in Purdue University and the other in Israel, coordinated their research efforts on different aspects of each objective utilizing both melons and tomatoes as case studies. At Purdue: An expert system was developed to measure variances in human grading. Data were acquired from eight sensors: vision, two firmness sensors (destructive and nondestructive), chlorophyll from fluorescence, color sensor, electronic sniffer for odor detecti

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2

Grigsby, Claude C., Ryan M. Kramer, Michael A. Zmuda, Derek W. Boone, Tyler C. Highlander, and Mateen M. Rizki. Differential Profiling of Volatile Organic Compound Biomarker Signatures Utilizing a Logical Statistical Filter-Set and Novel Hybrid Evolutionary Classifiers. Defense Technical Information Center, 2012. http://dx.doi.org/10.21236/ada562341.

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Desai, Jairaj, Jijo K. Mathew, Howell Li, Rahul Sakhare, Deborah Horton, and Darcy M. Bullock. National Mobility Analysis for All Interstate Routes in the United States. Purdue University, 2023. http://dx.doi.org/10.5703/1288284317585.

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Анотація:

In November 2022, Wejo Data Services Inc. provided Purdue with a national data set comprised of approximately 470 billion connected vehicle records covering all 50 states for the month of August 2022. The goal of the evaluation data set was to create a series of summary graphics to evaluate the scalability of work zone analytics graphics and electric/hybrid vehicle counts at a national level. This report illustrates several performance measures developed using this dataset for all interstate routes, both state wise and cross-country. State wise graphics are organized with 50 subdirectories con

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Kirichek, Galina, Vladyslav Harkusha, Artur Timenko, and Nataliia Kulykovska. System for detecting network anomalies using a hybrid of an uncontrolled and controlled neural network. [б. в.], 2020. http://dx.doi.org/10.31812/123456789/3743.

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In this article realization method of attacks and anomalies detection with the use of training of ordinary and attacking packages, respectively. The method that was used to teach an attack on is a combination of an uncontrollable and controlled neural network. In an uncontrolled network, attacks are classified in smaller categories, taking into account their features and using the self- organized map. To manage clusters, a neural network based on back-propagation method used. We use PyBrain as the main framework for designing, developing and learning perceptron data. This framework has a suffi

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Desai, Jairaj, Jijo K. Mathew, Howell Li, Rahul Suryakant Sakhare, Deborah Horton, and 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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Анотація:

In January 2023, Wejo Data Services Inc. provided Purdue with a national data set comprised of approximately 503 billion connected vehicle records covering all 50 states for the month of December 2022. The goal of the evaluation data set was to create a series of summary graphics to evaluate the scalability of work zone/winter weather analytics graphics and electric/hybrid vehicle counts at a national level as well as visualize the mobility impacts of winter weather activity on the national interstate network. This report illustrates several performance measures developed using this dataset fo

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De Man, Philip, Gustavo Müller, Marie Vandendriessche, Carlota Moreno Villar, Ana Aguilera Raga, and Jan Wouters. The Evolution of Global Governance. EsadeGeo. Center for Global Economy and Geopolitics, 2022. https://doi.org/10.56269/202209/pdm.

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Анотація:

This paper sets out to provide an overview of the evolution of global governance in key areas with security, defence and intelligence relevance, by mapping and analysing the evolution of eight case studies – trade, migration, outer space, health, energy, artificial intelligence, cybersecurity and climate change – over the past five to ten years. This approach allows the authors to identify key actors with which the EU should engage in multilateral settings. The paper finds that the role of traditional, formal intergovernmental organisations in the studied issue areas has shifted in recent year

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