Literatura académica sobre el tema "ANN Classifiers"
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Artículos de revistas sobre el tema "ANN Classifiers"
Mohamad, Mumtazimah, Wan Nor Shuhadah Wan Nik, Zahrahtul Amani Zakaria y Arifah Che Alhadi. "An Analysis of Large Data Classification using Ensemble Neural Network". International Journal of Engineering & Technology 7, n.º 2.14 (6 de abril de 2018): 53. http://dx.doi.org/10.14419/ijet.v7i2.14.11155.
Texto completoMahanya, G. B. y S. Nithyaselvakumari. "Analysis And Comparison Of Ventricular Cardiac Arrhythmia Classification Using Potassium Channel Parameters With ANN And KNN Classifier". CARDIOMETRY, n.º 25 (14 de febrero de 2023): 926–33. http://dx.doi.org/10.18137/cardiometry.2022.25.926933.
Texto completoMahanya, G. B. y S. Nithyaselvakumari. "Analysis And Comparison Of Ventricular Cardiac Arrhythmia Classification Using Sodium Channel Parameters With ANN And KNN Classifier". CARDIOMETRY, n.º 25 (14 de febrero de 2023): 911–18. http://dx.doi.org/10.18137/cardiometry.2022.25.911918.
Texto completoBenmouna, Brahim, Raziyeh Pourdarbani, Sajad Sabzi, Ruben Fernandez-Beltran, Ginés García-Mateos y José Miguel Molina-Martínez. "Comparison of Classic Classifiers, Metaheuristic Algorithms and Convolutional Neural Networks in Hyperspectral Classification of Nitrogen Treatment in Tomato Leaves". Remote Sensing 14, n.º 24 (16 de diciembre de 2022): 6366. http://dx.doi.org/10.3390/rs14246366.
Texto completoChang, Mahmud, Shin, Nguyen-Quang, Price y Prithiviraj. "Comparison of Image Texture Based Supervised Learning Classifiers for Strawberry Powdery Mildew Detection". AgriEngineering 1, n.º 3 (4 de septiembre de 2019): 434–52. http://dx.doi.org/10.3390/agriengineering1030032.
Texto completoPatgiri, Chayashree y Amrita Ganguly. "Machine Learning Techniques for Automatic Detection of Sickle Cell Anemia using Adaptive Thresholding and Contour-based Segmentation Method". Asian Pacific Journal of Health Sciences 9, n.º 4 (20 de junio de 2022): 165–70. http://dx.doi.org/10.21276/apjhs.2022.9.4.33.
Texto completoManjunatha, G. y H. C. Chittappa. "Bearing Fault Classification Using Statistical Features And Machine Learning Approach". Journal of Mines, Metals and Fuels 70, n.º 4 (1 de marzo de 2022): 104. http://dx.doi.org/10.18311/jmmf/2022/30676.
Texto completoManjunatha, G. y H. C. Chittappa. "Bearing Fault Classification Using Statistical Features And Machine Learning Approach". Journal of Mines, Metals and Fuels 70, n.º 3A (12 de julio de 2022): 104. http://dx.doi.org/10.18311/jmmf/2022/30687.
Texto completoMasood, Ibrahim, Nadia Zulikha Zainal Abidin, Nur Rashida Roshidi, Noor Azlina Rejab y Mohd Faizal Johari. "Design of an Artificial Neural Network Pattern Recognition Scheme Using Full Factorial Experiment". Applied Mechanics and Materials 465-466 (diciembre de 2013): 1149–54. http://dx.doi.org/10.4028/www.scientific.net/amm.465-466.1149.
Texto completoWang, Daliang y Xiaowen Guo. "Research on Intelligent Recognition and Classification Algorithm of Music Emotion in Complex System of Music Performance". Complexity 2021 (24 de junio de 2021): 1–10. http://dx.doi.org/10.1155/2021/4251827.
Texto completoTesis sobre el tema "ANN Classifiers"
Eldud, Omer Ahmed Abdelkarim. "Prediction of protein secondary structure using binary classificationtrees, naive Bayes classifiers and the Logistic Regression Classifier". Thesis, Rhodes University, 2016. http://hdl.handle.net/10962/d1019985.
Texto completoJoo, Hyonam. "Binary tree classifier and context classifier". Thesis, Virginia Polytechnic Institute and State University, 1985. http://hdl.handle.net/10919/53076.
Texto completoMaster of Science
Billing, Jeffrey J. (Jeffrey Joel) 1979. "Learning classifiers from medical data". Thesis, Massachusetts Institute of Technology, 2002. http://hdl.handle.net/1721.1/8068.
Texto completoIncludes bibliographical references (leaf 32).
The goal of this thesis was to use machine-learning techniques to discover classifiers from a database of medical data. Through the use of two software programs, C5.0 and SVMLight, we analyzed a database of 150 patients who had been operated on by Dr. David Rattner of the Massachusetts General Hospital. C5.0 is an algorithm that learns decision trees from data while SVMLight learns support vector machines from the data. With both techniques we performed cross-validation analysis and both failed to produce acceptable error rates. The end result of the research was that no classifiers could be found which performed well upon cross-validation analysis. Nonetheless, this paper provides a thorough examination of the different issues that arise during the analysis of medical data as well as describes the different techniques that were used as well as the different issues with the data that affected the performance of these techniques.
by Jeffrey J. Billing.
M.Eng.and S.B.
Siegel, Kathryn I. (Kathryn Iris). "Incremental random forest classifiers in spark". Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/106105.
Texto completoCataloged from PDF version of thesis.
Includes bibliographical references (page 53).
The random forest is a machine learning algorithm that has gained popularity due to its resistance to noise, good performance, and training efficiency. Random forests are typically constructed using a static dataset; to accommodate new data, random forests are usually regrown. This thesis presents two main strategies for updating random forests incrementally, rather than entirely rebuilding the forests. I implement these two strategies-incrementally growing existing trees and replacing old trees-in Spark Machine Learning(ML), a commonly used library for running ML algorithms in Spark. My implementation draws from existing methods in online learning literature, but includes several novel refinements. I evaluate the two implementations, as well as a variety of hybrid strategies, by recording their error rates and training times on four different datasets. My benchmarks show that the optimal strategy for incremental growth depends on the batch size and the presence of concept drift in a data workload. I find that workloads with large batches should be classified using a strategy that favors tree regrowth, while workloads with small batches should be classified using a strategy that favors incremental growth of existing trees. Overall, the system demonstrates significant efficiency gains when compared to the standard method of regrowing the random forest.
by Kathryn I. Siegel.
M. Eng.
Palmer-Brown, Dominic. "An adaptive resonance classifier". Thesis, University of Nottingham, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.334802.
Texto completoXue, Jinghao. "Aspects of generative and discriminative classifiers". Thesis, Connect to e-thesis, 2008. http://theses.gla.ac.uk/272/.
Texto completoPh.D. thesis submitted to the Department of Statistics, Faculty of Information and Mathematical Sciences, University of Glasgow, 2008. Includes bibliographical references. Print version also available.
Frankowsky, Maximilian y Dan Ke. "Humanness and classifiers in Mandarin Chinese". Universitätsbibliothek Leipzig, 2017. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-224789.
Texto completoLee, Yuchun. "Classifiers : adaptive modules in pattern recognition systems". Thesis, Massachusetts Institute of Technology, 1989. http://hdl.handle.net/1721.1/14496.
Texto completoChungfat, Neil C. (Neil Caye) 1979. "Context-aware activity recognition using TAN classifiers". Thesis, Massachusetts Institute of Technology, 2002. http://hdl.handle.net/1721.1/87220.
Texto completoIncludes bibliographical references (p. 73-77).
by Neil C. Chungfat.
M.Eng.
Li, Ming. "Sequence and text classification : features and classifiers". Thesis, University of East Anglia, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.426966.
Texto completoLibros sobre el tema "ANN Classifiers"
Raudys, Šarūnas. Statistical and Neural Classifiers. London: Springer London, 2001. http://dx.doi.org/10.1007/978-1-4471-0359-2.
Texto completoLearning and using Japanese numbers. Lincolnwood, Ill., USA: Passport Books, 1996.
Buscar texto completoSharma, C. D. Classified catalogue code in theory and practice. 2a ed. Jodhpur, India: Scientific Publishers, 1990.
Buscar texto completoLearning kernel classifiers: Theory and algorithms. Cambridge, Mass: MIT Press, 2002.
Buscar texto completoC, Carter Ruth, ed. Education and training for catalogers and classifiers. New York: Haworth Press, 1987.
Buscar texto completoCummings, James. Classified classics. Los Angeles: Price/Stern/Sloan, 1987.
Buscar texto completoDaniels, B. J. Classified Christmas. Toronto, Ontario: Harlequin, 2007.
Buscar texto completo(Army), Senior National Representatives. Defense, information exchange: Memorandum of understanding between the United States of America and other governments, signed at Washington, London, Paris and Bonn October 19, November 13 and 27, 1995 and January 9, 1996 with attachments and an understanding. Washington, D.C: Dept. of State, 2003.
Buscar texto completoXu, Dan, ed. Plurality and Classifiers across Languages in China. Berlin, Boston: DE GRUYTER, 2012. http://dx.doi.org/10.1515/9783110293982.
Texto completoXu, Dan. Plurality and classifiers across languages in China. Berlin: De Gruyter Mouton, 2012.
Buscar texto completoCapítulos de libros sobre el tema "ANN Classifiers"
Bakraouy, Zineb, Amine Baina y Mostafa Bellafkih. "Agreement-Broker: Performance Analysis Using KNN, SVM, and ANN Classifiers". En Advances in Intelligent Systems and Computing, 868–79. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-73689-7_82.
Texto completoDutta, Munmi, Chayashree Patgiri, Mousmita Sarma y Kandarpa Kumar Sarma. "Closed-Set Text-Independent Speaker Identification System Using Multiple ANN Classifiers". En Advances in Intelligent Systems and Computing, 377–85. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-11933-5_41.
Texto completoWalse, Kishor H., Rajiv V. Dharaskar y Vilas M. Thakare. "PCA Based Optimal ANN Classifiers for Human Activity Recognition Using Mobile Sensors Data". En Proceedings of First International Conference on Information and Communication Technology for Intelligent Systems: Volume 1, 429–36. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-30933-0_43.
Texto completoRashmi y Udayan Ghose. "Hybrid Entropy Method for Large Data Set Reduction Using MLP-ANN and SVM Classifiers". En Data Science and Analytics, 49–63. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5827-6_5.
Texto completoNiranjana Murthy, H. S. y M. Meenakshi. "Comparison between ANN-Based Heart Stroke Classifiers Using Varied Folds Data Set Cross-Validation". En Advances in Intelligent Systems and Computing, 693–99. New Delhi: Springer India, 2014. http://dx.doi.org/10.1007/978-81-322-2012-1_74.
Texto completoKim, Tae-Kyun y Roberto Cipolla. "Multiple Classifier Boosting and Tree-Structured Classifiers". En Machine Learning for Computer Vision, 163–96. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-28661-2_7.
Texto completoYang, Ai-min, Yong-mei Zhou y Min Tang. "A Classifier Ensemble Method for Fuzzy Classifiers". En Fuzzy Systems and Knowledge Discovery, 784–93. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11881599_97.
Texto completoJamnejad, Mohammad Iman, Sajad Parvin, Ali Heidarzadegan y Mohsen Moshki. "A Meta Classifier by Clustering of Classifiers". En Nature-Inspired Computation and Machine Learning, 140–51. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-13650-9_13.
Texto completoCai, ChenWei, Dickson Keddy Wornyo, Liangjun Wang y XiangJun Shen. "Building Weighted Classifier Ensembles Through Classifiers Pruning". En Communications in Computer and Information Science, 131–39. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-8530-7_13.
Texto completoRamos-Pollán, Raúl, Miguel Ángel Guevara-López y Eugénio Oliveira. "Introducing ROC Curves as Error Measure Functions: A New Approach to Train ANN-Based Biomedical Data Classifiers". En Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, 517–24. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16687-7_68.
Texto completoActas de conferencias sobre el tema "ANN Classifiers"
Aswin, K. S., Manav Purushothaman, Polisetty Sritharani y Angel T. S. "ANN and Deep Learning Classifiers for BCI applications". En 2022 Third International Conference on Intelligent Computing Instrumentation and Control Technologies (ICICICT). IEEE, 2022. http://dx.doi.org/10.1109/icicict54557.2022.9917834.
Texto completoKarimi, M., M. Banejad, H. Hassanpour y A. Moeini. "Classification of power system faults using ANN classifiers". En Energy Conference (IPEC 2010). IEEE, 2010. http://dx.doi.org/10.1109/ipecon.2010.5697048.
Texto completoEkbote, Juhi y Mahasweta Joshi. "Indian sign language recognition using ANN and SVM classifiers". En 2017 4th International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS). IEEE, 2017. http://dx.doi.org/10.1109/iciiecs.2017.8276111.
Texto completoOmrani, Takwa, Adel Dallali, Belgacem Chibani Rhaimi y Jaouhar Fattahi. "Fusion of ANN and SVM classifiers for network attack detection". En 2017 18th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA). IEEE, 2017. http://dx.doi.org/10.1109/sta.2017.8314974.
Texto completoAiordachioaie, Dorel. "On Thermal Image Classification with ANN and Similarity Based Classifiers". En 2022 International Conference on Communications, Information, Electronic and Energy Systems (CIEES). IEEE, 2022. http://dx.doi.org/10.1109/ciees55704.2022.9990680.
Texto completoIzquierdo, R., I. Parra, J. Munoz-Bulnes, D. Fernandez-Llorca y M. A. Sotelo. "Vehicle trajectory and lane change prediction using ANN and SVM classifiers". En 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC). IEEE, 2017. http://dx.doi.org/10.1109/itsc.2017.8317838.
Texto completoHarini, V., Nayana N. Patil y H. M. Rajashekar Swamy. "Comparison of Bayesian and ANN Classifiers for Crack Detection in Columns". En 2020 4th International Conference on Electronics, Materials Engineering & Nano-Technology (IEMENTech). IEEE, 2020. http://dx.doi.org/10.1109/iementech51367.2020.9270084.
Texto completoMahima y N. B. Padmavathi. "Comparative study of kernel SVM and ANN classifiers for brain neoplasm classification". En 2017 International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT). IEEE, 2017. http://dx.doi.org/10.1109/icicict1.2017.8342608.
Texto completoAkhmedova, Shakhnaz y Eugene Semenkin. "ANN-based Classifiers Automatically Generated by New Multi-objective Bionic Algorithm". En 12th International Conference on Informatics in Control, Automation and Robotics. SCITEPRESS - Science and and Technology Publications, 2015. http://dx.doi.org/10.5220/0005571603100317.
Texto completoSovierzoski, Miguel Antonio, Fernanda Isabel Marques Argoud y Fernando Mendes de Azevedo. "Evaluation of ANN Classifiers During Supervised Training with ROC Analysis and Cross Validation". En 2008 International Conference on Biomedical Engineering And Informatics (BMEI). IEEE, 2008. http://dx.doi.org/10.1109/bmei.2008.251.
Texto completoInformes sobre el tema "ANN Classifiers"
Searcy, Stephen W. y Kalman Peleg. Adaptive Sorting of Fresh Produce. United States Department of Agriculture, agosto de 1993. http://dx.doi.org/10.32747/1993.7568747.bard.
Texto completoUmunoza Gasana, Emelyne, Dietrich von Rosen y Martin Singull. Edgeworth-type expansion of the density of the classifier when growth curves are classified via likelihood. Linköping University Electronic Press, mayo de 2023. http://dx.doi.org/10.3384/lith-mat-r-2023-02.
Texto completoOstendorf, M., L. Atlas, R. Fish, O. Cetin, S. Sukittanon y G. D. Bernard. Joint Use of Dynamical Classifiers and Ambiguity Plane Features. Fort Belvoir, VA: Defense Technical Information Center, enero de 2001. http://dx.doi.org/10.21236/ada436824.
Texto completoDube, Arindrajit, Ethan Kaplan y Suresh Naidu. Coups, Corporations, and Classified Information. Cambridge, MA: National Bureau of Economic Research, abril de 2011. http://dx.doi.org/10.3386/w16952.
Texto completoRangwala, Huzefa y George Karypis. Building Multiclass Classifiers for Remote Homology Detection and Fold Recognition. Fort Belvoir, VA: Defense Technical Information Center, abril de 2006. http://dx.doi.org/10.21236/ada446086.
Texto completoWu, Jin Chu y Raghu N. Kacker. Standard Errors and Significance Testing in Data Analysis for Testing Classifiers. National Institute of Standards and Technology, julio de 2021. http://dx.doi.org/10.6028/nist.ir.8383.
Texto completoNelson, Bruce y Ammon Birenzvigo. Linguistic-Fuzzy Classifier for Discrimination and Confidence Value Estimation. Fort Belvoir, VA: Defense Technical Information Center, julio de 2004. http://dx.doi.org/10.21236/ada426951.
Texto completoHines, Paul C. y Carolyn M. Binder. Automatic Classification of Cetacean Vocalizations Using an Aural Classifier. Fort Belvoir, VA: Defense Technical Information Center, septiembre de 2012. http://dx.doi.org/10.21236/ada573485.
Texto completoHines, Paul C. y Carolyn M. Binder. Automatic Classification of Cetacean Vocalizations Using an Aural Classifier. Fort Belvoir, VA: Defense Technical Information Center, septiembre de 2013. http://dx.doi.org/10.21236/ada598331.
Texto completoHeisele, Bernd, Thomas Serre, Sayan Mukherjee y Tomaso Poggio. Feature Reduction and Hierarchy of Classifiers for Fast Object Detection in Video Images. Fort Belvoir, VA: Defense Technical Information Center, enero de 2001. http://dx.doi.org/10.21236/ada458821.
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