Literatura académica sobre el tema "Hybrid data mining"
Crea una cita precisa en los estilos APA, MLA, Chicago, Harvard y otros
Consulte las listas temáticas de artículos, libros, tesis, actas de conferencias y otras fuentes académicas sobre el tema "Hybrid data mining".
Junto a cada fuente en la lista de referencias hay un botón "Agregar a la bibliografía". Pulsa este botón, y generaremos automáticamente la referencia bibliográfica para la obra elegida en el estilo de cita que necesites: APA, MLA, Harvard, Vancouver, Chicago, etc.
También puede descargar el texto completo de la publicación académica en formato pdf y leer en línea su resumen siempre que esté disponible en los metadatos.
Artículos de revistas sobre el tema "Hybrid data mining"
Ambulkar, Bhagyashree y Prof Gunjan Agre. "Data Mining Over Encrypted Data of Database Client Engine Using Hybrid Classification Approach". International Journal of Innovative Research in Computer Science & Technology 5, n.º 3 (31 de mayo de 2017): 291–94. http://dx.doi.org/10.21276/ijircst.2017.5.3.7.
Texto completoElankavi, R., R. Kalaiprasath y R. Udayakumar. "DATA MINING WITH BIG DATA REVOLUTION HYBRID". International Journal on Smart Sensing and Intelligent Systems 10, n.º 4 (2017): 560–73. http://dx.doi.org/10.21307/ijssis-2017-270.
Texto completoLakshmi Devasena, C. y M. Hemalatha. "A Hybrid Image Mining Technique using LIMbased Data Mining Algorithm". International Journal of Computer Applications 25, n.º 2 (31 de julio de 2011): 1–5. http://dx.doi.org/10.5120/3007-4056.
Texto completoShadroo, Shabnam, Mohsen Yoosefi Nejad, Samira Tavanaiee Yosefian, Morteza Naserbakht y Mehdi Hosseinzadeh. "Proposing Two Hybrid Data Mining Models for Discovering Students' Mental Health Problems". Acta Informatica Pragensia 10, n.º 1 (30 de junio de 2021): 85–107. http://dx.doi.org/10.18267/j.aip.148.
Texto completoAzad, Chandrashekhar. "Data Mining based Hybrid Intrusion Detection System". Indian Journal of Science and Technology 7, n.º 6 (20 de junio de 2014): 781–89. http://dx.doi.org/10.17485/ijst/2014/v7i6.19.
Texto completoSharma, Monica y Rajdeep Kaur. "Data Mining in Healthcare using Hybrid Approach". International Journal of Computer Applications 128, n.º 4 (15 de octubre de 2015): 49–53. http://dx.doi.org/10.5120/ijca2015906539.
Texto completoAbidi, Balkis, Sadok Ben Yahia y Charith Perera. "Hybrid microaggregation for privacy preserving data mining". Journal of Ambient Intelligence and Humanized Computing 11, n.º 1 (26 de noviembre de 2018): 23–38. http://dx.doi.org/10.1007/s12652-018-1122-7.
Texto completoLee, Zne-Jung, Chou-Yuan Lee, So-Tsung Chou, Wei-Ping Ma, Fulan Ye y Zhen Chen. "A hybrid system for imbalanced data mining". Microsystem Technologies 26, n.º 9 (8 de agosto de 2019): 3043–47. http://dx.doi.org/10.1007/s00542-019-04566-1.
Texto completoPanda, Mrutyunjaya y Ajith Abraham. "Hybrid evolutionary algorithms for classification data mining". Neural Computing and Applications 26, n.º 3 (10 de agosto de 2014): 507–23. http://dx.doi.org/10.1007/s00521-014-1673-2.
Texto completoHarrag, Fouzi y Ali Alshehri. "Applying Data Mining in Surveillance". International Journal of Distributed Systems and Technologies 14, n.º 1 (10 de febrero de 2023): 1–24. http://dx.doi.org/10.4018/ijdst.317930.
Texto completoTesis sobre el tema "Hybrid data mining"
Daglar, Toprak Seda. "A New Hybrid Multi-relational Data Mining Technique". Master's thesis, METU, 2005. http://etd.lib.metu.edu.tr/upload/12606150/index.pdf.
Texto completoSeetan, Raed. "A Data Mining Approach to Radiation Hybrid Mapping". Diss., North Dakota State University, 2014. https://hdl.handle.net/10365/27315.
Texto completoZall, Davood. "Visual Data Mining : An Approach to Hybrid 3D Visualization". Thesis, Högskolan i Borås, Institutionen Handels- och IT-högskolan, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:hb:diva-16601.
Texto completoProgram: Magisterutbildning i informatik
Yang, Pengyi. "Ensemble methods and hybrid algorithms for computational and systems biology". Thesis, The University of Sydney, 2012. https://hdl.handle.net/2123/28979.
Texto completoTheobald, Claire. "Bayesian Deep Learning for Mining and Analyzing Astronomical Data". Electronic Thesis or Diss., Université de Lorraine, 2023. http://www.theses.fr/2023LORR0081.
Texto completoIn this thesis, we address the issue of trust in deep learning predictive systems in two complementary research directions. The first line of research focuses on the ability of AI to estimate its level of uncertainty in its decision-making as accurately as possible. The second line, on the other hand, focuses on the explainability of these systems, that is, their ability to convince human users of the soundness of their predictions.The problem of estimating the uncertainties is addressed from the perspective of Bayesian Deep Learning. Bayesian Neural Networks assume a probability distribution over their parameters, which allows them to estimate different types of uncertainties. First, aleatoric uncertainty which is related to the data, but also epistemic uncertainty which quantifies the lack of knowledge the model has on the data distribution. More specifically, this thesis proposes a Bayesian neural network can estimate these uncertainties in the context of a multivariate regression task. This model is applied to the regression of complex ellipticities on galaxy images as part of the ANR project "AstroDeep''. These images can be corrupted by different sources of perturbation and noise which can be reliably estimated by the different uncertainties. The exploitation of these uncertainties is then extended to galaxy mapping and then to "coaching'' the Bayesian neural network. This last technique consists of generating increasingly complex data during the model's training process to improve its performance.On the other hand, the problem of explainability is approached from the perspective of counterfactual explanations. These explanations consist of identifying what changes to the input parameters would have led to a different prediction. Our contribution in this field is based on the generation of counterfactual explanations relying on a variational autoencoder (VAE) and an ensemble of predictors trained on the latent space generated by the VAE. This method is particularly adapted to high-dimensional data, such as images. In this case, they are referred as counterfactual visual explanations. By exploiting both the latent space and the ensemble of classifiers, we can efficiently produce visual counterfactual explanations that reach a higher degree of realism than several state-of-the-art methods
Cheng, Xueqi. "Exploring Hybrid Dynamic and Static Techniques for Software Verification". Diss., Virginia Tech, 2010. http://hdl.handle.net/10919/26216.
Texto completoPh. D.
Viademonte, da Rosa Sérgio I. (Sérgio Ivan) 1964. "A hybrid model for intelligent decision support : combining data mining and artificial neural networks". Monash University, School of Information Management and Systems, 2004. http://arrow.monash.edu.au/hdl/1959.1/5159.
Texto completopande, anurag. "ESTIMATION OF HYBRID MODELS FOR REAL-TIME CRASH RISK ASSESSMENT ON FREEWAYS". Doctoral diss., University of Central Florida, 2005. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/3016.
Texto completoPh.D.
Department of Civil and Environmental Engineering
Engineering and Computer Science
Civil Engineering
Sainani, Varsha. "Hybrid Layered Intrusion Detection System". Scholarly Repository, 2009. http://scholarlyrepository.miami.edu/oa_theses/44.
Texto completoZhang, Jiapu. "Derivative-free hybrid methods in global optimization and their applications". Thesis, University of Ballarat, 2005. http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/34054.
Texto completoDoctor of Philosophy
Libros sobre el tema "Hybrid data mining"
Evgenii, Vityaev, ed. Data mining in finance: Advances in relational and hybrid methods. Boston: Kluwer Academic, 2000.
Buscar texto completoBergmeir, Philipp. Enhanced Machine Learning and Data Mining Methods for Analysing Large Hybrid Electric Vehicle Fleets based on Load Spectrum Data. Wiesbaden: Springer Fachmedien Wiesbaden, 2018. http://dx.doi.org/10.1007/978-3-658-20367-2.
Texto completoDaniel, Howard, Ślęzak Dominik, Hong You Sik y SpringerLink (Online service), eds. Convergence and Hybrid Information Technology: 6th International Conference, ICHIT 2012, Daejeon, Korea, August 23-25, 2012. Proceedings. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.
Buscar texto completoSifeng, Liu y Lin Yi 1959-, eds. Hybrid rough sets and applications in uncertain decision-making. Boca Raton: Auerbach Publications, 2010.
Buscar texto completoLee, Geuk. Convergence and Hybrid Information Technology: 6th International Conference, ICHIT 2012, Daejeon, Korea, August 23-25, 2012. Proceedings. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.
Buscar texto completoDaniel, Howard, Kim Haeng-kon, Kim Tai-hoon, Ko Il-seok, Lee Geuk, Ślęzak Dominik, Sloot Peter 1956- y SpringerLink (Online service), eds. Advances in Hybrid Information Technology: First International Conference, ICHIT 2006, Jeju Island, Korea, November 9-11, 2006, Revised Selected Papers. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2007.
Buscar texto completoEmilio, Corchado, Abraham Ajith 1968- y Pedrycz Witold 1953-, eds. Hybrid artificial intelligence systems: Third international workshop, HAIS 2008, Burgos, Spain, September 24-26, 2008 : proceedings. Berlin: Springer, 2008.
Buscar texto completoDaniel, Howard, Ślęzak Dominik y SpringerLink (Online service), eds. Convergence and Hybrid Information Technology: 5th International Conference, ICHIT 2011, Daejeon, Korea, September 22-24, 2011. Proceedings. Berlin, Heidelberg: Springer-Verlag GmbH Berlin Heidelberg, 2011.
Buscar texto completoLee, Geuk. Convergence and Hybrid Information Technology: 5th International Conference, ICHIT 2011, Daejeon, Korea, September 22-24, 2011. Proceedings. Berlin, Heidelberg: Springer-Verlag GmbH Berlin Heidelberg, 2011.
Buscar texto completoDavid, Hutchison. Hybrid Artificial Intelligence Systems: 4th International Conference, HAIS 2009, Salamanca, Spain, June 10-12, 2009. Proceedings. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009.
Buscar texto completoCapítulos de libros sobre el tema "Hybrid data mining"
Dani, Virendra, Priyanka Kokate, Surbhi Kushwah y Swapnil Waghela. "Privacy Preserving Data Mining Technique to Secure Distributed Client Data". En Hybrid Intelligent Systems, 565–74. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-96305-7_52.
Texto completoDu, Mingjing y Shifei Ding. "L-DP: A Hybrid Density Peaks Clustering Method". En Data Mining and Big Data, 74–80. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-61845-6_8.
Texto completoSonawani, Shilpa y Amrita Mishra. "DHPTID-HYBRID Algorithm: A Hybrid Algorithm for Association Rule Mining". En Advanced Data Mining and Applications, 149–60. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-17316-5_14.
Texto completoMucherino, A. y L. Liberti. "A VNS-Based Heuristic for Feature Selection in Data Mining". En Hybrid Metaheuristics, 353–68. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-30671-6_13.
Texto completoSmith-Miles, Kate, Brendan Wreford, Leo Lopes y Nur Insani. "Predicting Metaheuristic Performance on Graph Coloring Problems Using Data Mining". En Hybrid Metaheuristics, 417–32. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-30671-6_16.
Texto completoLi, Kan, Wensi Mu, Yong Luan y Shaohua An. "A Hybrid-Sorting Semantic Matching Method". En Advanced Data Mining and Applications, 404–13. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-53917-6_36.
Texto completoShafiq, Sobia, Wasi Haider Butt y Usman Qamar. "Attack Type Prediction Using Hybrid Classifier". En Advanced Data Mining and Applications, 488–98. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-14717-8_38.
Texto completoCecotti, Hubert y Abdel Belaïd. "Hybrid OCR Combination for Ancient Documents". En Pattern Recognition and Data Mining, 646–53. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11551188_71.
Texto completoLee, Jae Sik y Jin Chun Lee. "Customer Churn Prediction by Hybrid Model". En Advanced Data Mining and Applications, 959–66. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11811305_104.
Texto completoRakotomalala, Ricco, Faouzi Mhamdi y Mourad Elloumi. "Hybrid Feature Ranking for Proteins Classification". En Advanced Data Mining and Applications, 610–17. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11527503_72.
Texto completoActas de conferencias sobre el tema "Hybrid data mining"
Grzymala-Busse, J. W., Z. S. Hippe, T. Mroczek, E. Roj y B. Skowronski. "Data mining experiments on hop processing data". En Fifth International Conference on Hybrid Intelligent Systems (HIS'05). IEEE, 2005. http://dx.doi.org/10.1109/ichis.2005.32.
Texto completoTiwari, Anil Kumar, G. Ramakrishna, Lokesh Kumar Sharma y Sunil Kumar Kashyap. "Neural Network and Genetic Algorithm based Hybrid Data Mining Algorithm (Hybrid Data Mining Algorithm)". En 2019 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS). IEEE, 2019. http://dx.doi.org/10.1109/icccis48478.2019.8974485.
Texto completoChung, Sheng-Hao, Wei-Han Chang y Kawuu W. Lin. "A data mining algorithm for mining region-aware cyclic patterns". En 2011 11th International Conference on Hybrid Intelligent Systems (HIS 2011). IEEE, 2011. http://dx.doi.org/10.1109/his.2011.6122195.
Texto completoHambaba, M. L. "Intelligent hybrid system for data mining". En IEEE/IAFE 1996 Conference on Computational Intelligence for Financial Engineering (CIFEr). IEEE, 1996. http://dx.doi.org/10.1109/cifer.1996.501832.
Texto completoSuraj, Z. y Delimata. "Data Mining Exploration System for Feature Selection Tasks". En 2006 International Conference on Hybrid Information Technology. IEEE, 2006. http://dx.doi.org/10.1109/ichit.2006.253500.
Texto completoHadzic, F., H. Tan, T. S. Dillon y E. Chang. "Implications of frequent subtree mining using hybrid support definition". En DATA MINING & INFORMATION ENGINEERING 2007. Southampton, UK: WIT Press, 2007. http://dx.doi.org/10.2495/data070021.
Texto completoXydas, S., A. S. Hassan, C. E. Marmaras, N. Jenkins y L. M. Cipcigan. "Electric Vehicle Load Forecasting using Data Mining Methods". En Hybrid and Electric Vehicles Conference 2013 (HEVC 2013). Institution of Engineering and Technology, 2013. http://dx.doi.org/10.1049/cp.2013.1914.
Texto completoChen, Chunying, Xiongwei Zhou y Jianzhong Zhang. "Web Data Mining System Based on Web Services". En 2009 Ninth International Conference on Hybrid Intelligent Systems. IEEE, 2009. http://dx.doi.org/10.1109/his.2009.258.
Texto completoPutri, Awalia W. y Laksmiwati Hira. "Hybrid transformation in privacy-preserving data mining". En 2016 International Conference on Data and Software Engineering (ICoDSE). IEEE, 2016. http://dx.doi.org/10.1109/icodse.2016.7936114.
Texto completoBellary, Jyothi, Bhargavi Peyakunta y Sekhar Konetigari. "Hybrid Machine Learning Approach in Data Mining". En 2010 Second International Conference on Machine Learning and Computing. IEEE, 2010. http://dx.doi.org/10.1109/icmlc.2010.57.
Texto completo