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Статті в журналах з теми "ANN Classifiers"
Mohamad, Mumtazimah, Wan Nor Shuhadah Wan Nik, Zahrahtul Amani Zakaria, and Arifah Che Alhadi. "An Analysis of Large Data Classification using Ensemble Neural Network." International Journal of Engineering & Technology 7, no. 2.14 (April 6, 2018): 53. http://dx.doi.org/10.14419/ijet.v7i2.14.11155.
Повний текст джерелаMahanya, G. B., and S. Nithyaselvakumari. "Analysis And Comparison Of Ventricular Cardiac Arrhythmia Classification Using Potassium Channel Parameters With ANN And KNN Classifier." CARDIOMETRY, no. 25 (February 14, 2023): 926–33. http://dx.doi.org/10.18137/cardiometry.2022.25.926933.
Повний текст джерелаMahanya, G. B., and S. Nithyaselvakumari. "Analysis And Comparison Of Ventricular Cardiac Arrhythmia Classification Using Sodium Channel Parameters With ANN And KNN Classifier." CARDIOMETRY, no. 25 (February 14, 2023): 911–18. http://dx.doi.org/10.18137/cardiometry.2022.25.911918.
Повний текст джерелаBenmouna, Brahim, Raziyeh Pourdarbani, Sajad Sabzi, Ruben Fernandez-Beltran, Ginés García-Mateos, and 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, no. 24 (December 16, 2022): 6366. http://dx.doi.org/10.3390/rs14246366.
Повний текст джерелаChang, Mahmud, Shin, Nguyen-Quang, Price, and Prithiviraj. "Comparison of Image Texture Based Supervised Learning Classifiers for Strawberry Powdery Mildew Detection." AgriEngineering 1, no. 3 (September 4, 2019): 434–52. http://dx.doi.org/10.3390/agriengineering1030032.
Повний текст джерелаPatgiri, Chayashree, and 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, no. 4 (June 20, 2022): 165–70. http://dx.doi.org/10.21276/apjhs.2022.9.4.33.
Повний текст джерелаManjunatha, G., and H. C. Chittappa. "Bearing Fault Classification Using Statistical Features And Machine Learning Approach." Journal of Mines, Metals and Fuels 70, no. 4 (March 1, 2022): 104. http://dx.doi.org/10.18311/jmmf/2022/30676.
Повний текст джерелаManjunatha, G., and H. C. Chittappa. "Bearing Fault Classification Using Statistical Features And Machine Learning Approach." Journal of Mines, Metals and Fuels 70, no. 3A (July 12, 2022): 104. http://dx.doi.org/10.18311/jmmf/2022/30687.
Повний текст джерелаMasood, Ibrahim, Nadia Zulikha Zainal Abidin, Nur Rashida Roshidi, Noor Azlina Rejab, and Mohd Faizal Johari. "Design of an Artificial Neural Network Pattern Recognition Scheme Using Full Factorial Experiment." Applied Mechanics and Materials 465-466 (December 2013): 1149–54. http://dx.doi.org/10.4028/www.scientific.net/amm.465-466.1149.
Повний текст джерелаWang, Daliang, and Xiaowen Guo. "Research on Intelligent Recognition and Classification Algorithm of Music Emotion in Complex System of Music Performance." Complexity 2021 (June 24, 2021): 1–10. http://dx.doi.org/10.1155/2021/4251827.
Повний текст джерелаДисертації з теми "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.
Повний текст джерелаJoo, Hyonam. "Binary tree classifier and context classifier." Thesis, Virginia Polytechnic Institute and State University, 1985. http://hdl.handle.net/10919/53076.
Повний текст джерелаMaster 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.
Повний текст джерелаIncludes 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.
Повний текст джерелаCataloged 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.
Повний текст джерелаXue, Jinghao. "Aspects of generative and discriminative classifiers." Thesis, Connect to e-thesis, 2008. http://theses.gla.ac.uk/272/.
Повний текст джерелаPh.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, and Dan Ke. "Humanness and classifiers in Mandarin Chinese." Universitätsbibliothek Leipzig, 2017. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-224789.
Повний текст джерелаLee, Yuchun. "Classifiers : adaptive modules in pattern recognition systems." Thesis, Massachusetts Institute of Technology, 1989. http://hdl.handle.net/1721.1/14496.
Повний текст джерелаChungfat, 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.
Повний текст джерелаIncludes 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.
Повний текст джерелаКниги з теми "ANN Classifiers"
Raudys, Šarūnas. Statistical and Neural Classifiers. London: Springer London, 2001. http://dx.doi.org/10.1007/978-1-4471-0359-2.
Повний текст джерелаLearning and using Japanese numbers. Lincolnwood, Ill., USA: Passport Books, 1996.
Знайти повний текст джерелаSharma, C. D. Classified catalogue code in theory and practice. 2nd ed. Jodhpur, India: Scientific Publishers, 1990.
Знайти повний текст джерелаLearning kernel classifiers: Theory and algorithms. Cambridge, Mass: MIT Press, 2002.
Знайти повний текст джерелаC, Carter Ruth, ed. Education and training for catalogers and classifiers. New York: Haworth Press, 1987.
Знайти повний текст джерелаCummings, James. Classified classics. Los Angeles: Price/Stern/Sloan, 1987.
Знайти повний текст джерелаDaniels, B. J. Classified Christmas. Toronto, Ontario: Harlequin, 2007.
Знайти повний текст джерела(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.
Знайти повний текст джерелаXu, Dan, ed. Plurality and Classifiers across Languages in China. Berlin, Boston: DE GRUYTER, 2012. http://dx.doi.org/10.1515/9783110293982.
Повний текст джерелаXu, Dan. Plurality and classifiers across languages in China. Berlin: De Gruyter Mouton, 2012.
Знайти повний текст джерелаЧастини книг з теми "ANN Classifiers"
Bakraouy, Zineb, Amine Baina, and Mostafa Bellafkih. "Agreement-Broker: Performance Analysis Using KNN, SVM, and ANN Classifiers." In 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.
Повний текст джерелаDutta, Munmi, Chayashree Patgiri, Mousmita Sarma, and Kandarpa Kumar Sarma. "Closed-Set Text-Independent Speaker Identification System Using Multiple ANN Classifiers." In 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.
Повний текст джерелаWalse, Kishor H., Rajiv V. Dharaskar, and Vilas M. Thakare. "PCA Based Optimal ANN Classifiers for Human Activity Recognition Using Mobile Sensors Data." In 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.
Повний текст джерелаRashmi and Udayan Ghose. "Hybrid Entropy Method for Large Data Set Reduction Using MLP-ANN and SVM Classifiers." In Data Science and Analytics, 49–63. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5827-6_5.
Повний текст джерелаNiranjana Murthy, H. S., and M. Meenakshi. "Comparison between ANN-Based Heart Stroke Classifiers Using Varied Folds Data Set Cross-Validation." In 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.
Повний текст джерелаKim, Tae-Kyun, and Roberto Cipolla. "Multiple Classifier Boosting and Tree-Structured Classifiers." In 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.
Повний текст джерелаYang, Ai-min, Yong-mei Zhou, and Min Tang. "A Classifier Ensemble Method for Fuzzy Classifiers." In Fuzzy Systems and Knowledge Discovery, 784–93. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11881599_97.
Повний текст джерелаJamnejad, Mohammad Iman, Sajad Parvin, Ali Heidarzadegan, and Mohsen Moshki. "A Meta Classifier by Clustering of Classifiers." In 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.
Повний текст джерелаCai, ChenWei, Dickson Keddy Wornyo, Liangjun Wang, and XiangJun Shen. "Building Weighted Classifier Ensembles Through Classifiers Pruning." In Communications in Computer and Information Science, 131–39. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-8530-7_13.
Повний текст джерелаRamos-Pollán, Raúl, Miguel Ángel Guevara-López, and Eugénio Oliveira. "Introducing ROC Curves as Error Measure Functions: A New Approach to Train ANN-Based Biomedical Data Classifiers." In 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.
Повний текст джерелаТези доповідей конференцій з теми "ANN Classifiers"
Aswin, K. S., Manav Purushothaman, Polisetty Sritharani, and Angel T. S. "ANN and Deep Learning Classifiers for BCI applications." In 2022 Third International Conference on Intelligent Computing Instrumentation and Control Technologies (ICICICT). IEEE, 2022. http://dx.doi.org/10.1109/icicict54557.2022.9917834.
Повний текст джерелаKarimi, M., M. Banejad, H. Hassanpour, and A. Moeini. "Classification of power system faults using ANN classifiers." In Energy Conference (IPEC 2010). IEEE, 2010. http://dx.doi.org/10.1109/ipecon.2010.5697048.
Повний текст джерелаEkbote, Juhi, and Mahasweta Joshi. "Indian sign language recognition using ANN and SVM classifiers." In 2017 4th International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS). IEEE, 2017. http://dx.doi.org/10.1109/iciiecs.2017.8276111.
Повний текст джерелаOmrani, Takwa, Adel Dallali, Belgacem Chibani Rhaimi, and Jaouhar Fattahi. "Fusion of ANN and SVM classifiers for network attack detection." In 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.
Повний текст джерелаAiordachioaie, Dorel. "On Thermal Image Classification with ANN and Similarity Based Classifiers." In 2022 International Conference on Communications, Information, Electronic and Energy Systems (CIEES). IEEE, 2022. http://dx.doi.org/10.1109/ciees55704.2022.9990680.
Повний текст джерелаIzquierdo, R., I. Parra, J. Munoz-Bulnes, D. Fernandez-Llorca, and M. A. Sotelo. "Vehicle trajectory and lane change prediction using ANN and SVM classifiers." In 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC). IEEE, 2017. http://dx.doi.org/10.1109/itsc.2017.8317838.
Повний текст джерелаHarini, V., Nayana N. Patil, and H. M. Rajashekar Swamy. "Comparison of Bayesian and ANN Classifiers for Crack Detection in Columns." In 2020 4th International Conference on Electronics, Materials Engineering & Nano-Technology (IEMENTech). IEEE, 2020. http://dx.doi.org/10.1109/iementech51367.2020.9270084.
Повний текст джерелаMahima and N. B. Padmavathi. "Comparative study of kernel SVM and ANN classifiers for brain neoplasm classification." In 2017 International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT). IEEE, 2017. http://dx.doi.org/10.1109/icicict1.2017.8342608.
Повний текст джерелаAkhmedova, Shakhnaz, and Eugene Semenkin. "ANN-based Classifiers Automatically Generated by New Multi-objective Bionic Algorithm." In 12th International Conference on Informatics in Control, Automation and Robotics. SCITEPRESS - Science and and Technology Publications, 2015. http://dx.doi.org/10.5220/0005571603100317.
Повний текст джерелаSovierzoski, Miguel Antonio, Fernanda Isabel Marques Argoud, and Fernando Mendes de Azevedo. "Evaluation of ANN Classifiers During Supervised Training with ROC Analysis and Cross Validation." In 2008 International Conference on Biomedical Engineering And Informatics (BMEI). IEEE, 2008. http://dx.doi.org/10.1109/bmei.2008.251.
Повний текст джерелаЗвіти організацій з теми "ANN Classifiers"
Searcy, Stephen W., and Kalman Peleg. Adaptive Sorting of Fresh Produce. United States Department of Agriculture, August 1993. http://dx.doi.org/10.32747/1993.7568747.bard.
Повний текст джерелаUmunoza Gasana, Emelyne, Dietrich von Rosen, and Martin Singull. Edgeworth-type expansion of the density of the classifier when growth curves are classified via likelihood. Linköping University Electronic Press, May 2023. http://dx.doi.org/10.3384/lith-mat-r-2023-02.
Повний текст джерелаOstendorf, M., L. Atlas, R. Fish, O. Cetin, S. Sukittanon, and G. D. Bernard. Joint Use of Dynamical Classifiers and Ambiguity Plane Features. Fort Belvoir, VA: Defense Technical Information Center, January 2001. http://dx.doi.org/10.21236/ada436824.
Повний текст джерелаDube, Arindrajit, Ethan Kaplan, and Suresh Naidu. Coups, Corporations, and Classified Information. Cambridge, MA: National Bureau of Economic Research, April 2011. http://dx.doi.org/10.3386/w16952.
Повний текст джерелаRangwala, Huzefa, and George Karypis. Building Multiclass Classifiers for Remote Homology Detection and Fold Recognition. Fort Belvoir, VA: Defense Technical Information Center, April 2006. http://dx.doi.org/10.21236/ada446086.
Повний текст джерелаWu, Jin Chu, and Raghu N. Kacker. Standard Errors and Significance Testing in Data Analysis for Testing Classifiers. National Institute of Standards and Technology, July 2021. http://dx.doi.org/10.6028/nist.ir.8383.
Повний текст джерелаNelson, Bruce, and Ammon Birenzvigo. Linguistic-Fuzzy Classifier for Discrimination and Confidence Value Estimation. Fort Belvoir, VA: Defense Technical Information Center, July 2004. http://dx.doi.org/10.21236/ada426951.
Повний текст джерелаHines, Paul C., and Carolyn M. Binder. Automatic Classification of Cetacean Vocalizations Using an Aural Classifier. Fort Belvoir, VA: Defense Technical Information Center, September 2012. http://dx.doi.org/10.21236/ada573485.
Повний текст джерелаHines, Paul C., and Carolyn M. Binder. Automatic Classification of Cetacean Vocalizations Using an Aural Classifier. Fort Belvoir, VA: Defense Technical Information Center, September 2013. http://dx.doi.org/10.21236/ada598331.
Повний текст джерелаHeisele, Bernd, Thomas Serre, Sayan Mukherjee, and Tomaso Poggio. Feature Reduction and Hierarchy of Classifiers for Fast Object Detection in Video Images. Fort Belvoir, VA: Defense Technical Information Center, January 2001. http://dx.doi.org/10.21236/ada458821.
Повний текст джерела