Literatura académica sobre el tema "Classification based on generative models"
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Artículos de revistas sobre el tema "Classification based on generative models"
Cazzanti, Luca, Maya R. Gupta y Anjali J. Koppal. "Generative models for similarity-based classification". Pattern Recognition 41, n.º 7 (julio de 2008): 2289–97. http://dx.doi.org/10.1016/j.patcog.2008.01.005.
Texto completoWei, Wei, Jun Fang, Ning Yang, Qi Li, Lin Hu, Lanbo Zhao y Jie Han. "AC-ModNet: Molecular Reverse Design Network Based on Attribute Classification". International Journal of Molecular Sciences 25, n.º 13 (25 de junio de 2024): 6940. http://dx.doi.org/10.3390/ijms25136940.
Texto completoGopal, Narendra y Sivakumar D. "DIMENSIONALITY REDUCTION BASED CLASSIFICATION USING GENERATIVE ADVERSARIAL NETWORKS DATASET GENERATION". ICTACT Journal on Image and Video Processing 13, n.º 01 (1 de agosto de 2022): 2786–90. http://dx.doi.org/10.21917/ijivp.2022.0396.
Texto completoShastry, K. Aditya, B. A. Manjunatha, T. G. Mohan Kumar y D. U. Karthik. "Generative Adversarial Networks Based Scene Generation on Indian Driving Dataset". Journal of ICT Research and Applications 17, n.º 2 (31 de agosto de 2023): 181–200. http://dx.doi.org/10.5614/itbj.ict.res.appl.2023.17.2.4.
Texto completoEkolle, Zie Eya y Ryuji Kohno. "GenCo: A Generative Learning Model for Heterogeneous Text Classification Based on Collaborative Partial Classifications". Applied Sciences 13, n.º 14 (14 de julio de 2023): 8211. http://dx.doi.org/10.3390/app13148211.
Texto completoZhai, Junhai, Jiaxing Qi y Chu Shen. "Binary imbalanced data classification based on diversity oversampling by generative models". Information Sciences 585 (marzo de 2022): 313–43. http://dx.doi.org/10.1016/j.ins.2021.11.058.
Texto completoKim, Eunbeen, Jaeuk Moon, Jonghwa Shim y Eenjun Hwang. "DualDiscWaveGAN-Based Data Augmentation Scheme for Animal Sound Classification". Sensors 23, n.º 4 (10 de febrero de 2023): 2024. http://dx.doi.org/10.3390/s23042024.
Texto completoKannan, K. Gokul y T. R. Ganesh Babu. "Semi Supervised Generative Adversarial Network for Automated Glaucoma Diagnosis with Stacked Discriminator Models". Journal of Medical Imaging and Health Informatics 11, n.º 5 (1 de mayo de 2021): 1334–40. http://dx.doi.org/10.1166/jmihi.2021.3787.
Texto completoChen, Zirui. "Diffusion Models-based Data Augmentation for the Cell Cycle Phase Classification". Journal of Physics: Conference Series 2580, n.º 1 (1 de septiembre de 2023): 012001. http://dx.doi.org/10.1088/1742-6596/2580/1/012001.
Texto completoBhavani, N. Sree, G. Narendra Babu Reddy, Y. Sravani Devi, M. Bhavani, P. Chandana Reddy y V. Abhignya Reddy. "Generative Data Augmentation and ARMD Classification". International Journal for Research in Applied Science and Engineering Technology 11, n.º 6 (30 de junio de 2023): 3662–67. http://dx.doi.org/10.22214/ijraset.2023.54178.
Texto completoTesis sobre el tema "Classification based on generative models"
Cazzanti, Luca. "Generative models of similarity-based classification /". Thesis, Connect to this title online; UW restricted, 2007. http://hdl.handle.net/1773/5905.
Texto completoLjungberg, Lucas. "Using unsupervised classification with multiple LDA derived models for text generation based on noisy and sensitive data". Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-255010.
Texto completoAtt skapa modeller som genererar kontextuella svar på frågor är ett svårt problem från början, någonting som blir än mer svårt när tillgänglig data innehåller både brus och känslig information. Det är både viktigt och av stort intresse att hitta modeller och metoder som kan hantera dessa svårigheter så att även problematisk data kan användas produktivt.Detta examensarbete föreslår en modell baserat på ett par samarbetande Topic Models (ämnesbaserade modeller) med skiljande ansvarsområden (LDA och GSDMM) för att underlätta de problematiska egenskaperna av datan. Modellen testas på ett verkligt dataset med dessa svårigheter samt ett dataset utan dessa. Målet är att 1) inspektera båda ämnesmodellernas beteende för att se om dessa kan representera datan på ett sådant sätt att andra modeller kan använda dessa som indata eller utdata och 2) förstå vilka av dessa svårigheter som kan hanteras som följd.Resultaten visar att ämnesmodellerna kan representera semantiken och betydelsen av dokument bra nog för att producera välartad indata för andra modeller. Denna representation kan även hantera stora ordlistor och brus i texten. Resultaten visar även att ämnesgrupperingen av responsdatan är godartad nog att användas som mål för klassificeringsmodeller sådant att korrekta meningar kan genereras som respons.
Malazizi, Ladan. "Development of Artificial Intelligence-based In-Silico Toxicity Models. Data Quality Analysis and Model Performance Enhancement through Data Generation". Thesis, University of Bradford, 2008. http://hdl.handle.net/10454/4262.
Texto completoBornelöv, Susanne. "Rule-based Models of Transcriptional Regulation and Complex Diseases : Applications and Development". Doctoral thesis, Uppsala universitet, Beräknings- och systembiologi, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-230159.
Texto completoHaghebaert, Marie. "Outils et méthodes pour la modélisation de la dynamique des écosystèmes microbiens complexes à partir d'observations expérimentales temporelles : application à la dynamique du microbiote intestinal". Electronic Thesis or Diss., université Paris-Saclay, 2023. http://www.theses.fr/2023UPASM036.
Texto completoThis thesis stems from the European project Homo.symbiosus, which investigates the equilibrium transitions of interactions between the host and its intestinal microbiota. To study these transitions, we pursue two directions: the mechanistic modeling of host-microbiota interactions and the analysis of temporal microbial count data.We enriched and simulated a deterministic model of the intestinal crypt using the EDK numerical scheme, particularly studying the impact of different parameters using the Morris Elementary Effects method. This model proved capable of simulating, on one hand, symbiotic and dysbiotic interaction states and, on the other hand, transition scenarios between states of dysbiosis and symbiosis.In parallel, a compartmental ODE model of the colon, inspired by existing studies, was developed and coupled with the crypt model. The thesis contributed to the enhancement of bacterial metabolism modeling and the modeling of innate immunity at the scale of the intestinal mucosa. A numerical exploration allowed us to assess the influence of diet on the steady state of the model and to study the effect of a pathological scenario by mimicking a breach in the epithelial barrier.Furthermore, we developed an approach to analyze microbial data aimed at assessing the deviation of microbial ecosystems undergoing significant environmental disturbances compared to a reference state. This method, based on DMM classification, enables the study of ecosystem equilibrium transitions in cases with few individuals and few time points. Moreover, a curve classification method using the SBM model was applied to investigate the effects of various disturbances on the microbial ecosystem; the results from this study were used to enrich the host-microbiota interaction model
Müller, Richard. "Software Visualization in 3D". Doctoral thesis, Universitätsbibliothek Leipzig, 2015. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-164699.
Texto completoOzer, Gizem. "Fuzzy Classification Models Based On Tanaka". Master's thesis, METU, 2009. http://etd.lib.metu.edu.tr/upload/12610785/index.pdf.
Texto completos Fuzzy Linear Regression (FLR) approach, and an improvement of an existing one, Improved Fuzzy Classifier Functions (IFCF). Tanaka&rsquo
s FLR approach is a well known fuzzy regression technique used for the prediction problems including fuzzy type of uncertainty. In the first part of the study, three alternative approaches are presented, which utilize the FLR approach for a particular customer satisfaction classification problem. A comparison of their performances and their applicability in other cases are discussed. In the second part of the study, the improved IFCF method, Nonparametric Improved Fuzzy Classifier Functions (NIFCF), is presented, which proposes to use a nonparametric method, Multivariate Adaptive Regression Splines (MARS), in clustering phase of the IFCF method. NIFCF method is applied on three data sets, and compared with Fuzzy Classifier Function (FCF) and Logistic Regression (LR) methods.
Elzobi, Moftah M. [Verfasser]. "Unconstrained recognition of offline Arabic handwriting using generative and discriminative classification models / Moftah M. Elzobi". Magdeburg : Universitätsbibliothek, 2017. http://d-nb.info/1135662185/34.
Texto completoSantiago, Dionny. "A Model-Based AI-Driven Test Generation System". FIU Digital Commons, 2018. https://digitalcommons.fiu.edu/etd/3878.
Texto completoBirks, Daniel J. "Computational Agent-Based Models of Offending: Assessing the Generative Sufficiency of Opportunity-Based Explanations of the Crime Event". Thesis, Griffith University, 2012. http://hdl.handle.net/10072/367327.
Texto completoThesis (PhD Doctorate)
Doctor of Philosophy (PhD)
School of Criminology and Criminal Justice
Arts, Education and Law
Full Text
Libros sobre el tema "Classification based on generative models"
Epstein, Joshua M. Generative social science: Studies in agent-based computational modeling. Princeton: Princeton University Press, 2006.
Buscar texto completoMackay, David Scott. Knowledge based classification of higher order terrain objects on digital elevation models. Ottawa: National Library of Canada = Bibliothèque nationale du Canada, 1991.
Buscar texto completoMarchenko, Aleksey y Mihail Nemcov. Electronics. ru: INFRA-M Academic Publishing LLC., 2023. http://dx.doi.org/10.12737/1587595.
Texto completoSerebryakov, Andrey y Gennadiy Zhuravlev. Exploitation of oil and gas fields by horizontal wells. ru: INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/971768.
Texto completoSerebryakov, Andrey, Lyubov' Ushivceva, Viktor Pyhalov y Zhanetta Kalashnik. Calculation of geological reserves and resources of oil, gas, condensate and commercial products. ru: INFRA-M Academic Publishing LLC., 2022. http://dx.doi.org/10.12737/1225035.
Texto completoVasil'eva, Natal'ya. Mathematical models in the management of copper production: ideas, methods, examples. ru: INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1014071.
Texto completoAstaf'eva, Ol'ga, Natal'ya Moiseenko, Aleksandr Kozlovskiy, Tat'yana Shemyakina y Viktor Serov. Risk management in construction. ru: INFRA-M Academic Publishing LLC., 2022. http://dx.doi.org/10.12737/1842952.
Texto completoNaumov, Vladimir. Consumer behavior. ru: INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1014653.
Texto completoBogumil, Veniamin y Sarango Duke. Telematics on urban passenger transport. ru: INFRA-M Academic Publishing LLC., 2022. http://dx.doi.org/10.12737/1819882.
Texto completoCevelev, Aleksandr. Material management of railway transport. ru: INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1064961.
Texto completoCapítulos de libros sobre el tema "Classification based on generative models"
Akrout, Mohamed, Bálint Gyepesi, Péter Holló, Adrienn Poór, Blága Kincső, Stephen Solis, Katrina Cirone et al. "Diffusion-Based Data Augmentation for Skin Disease Classification: Impact Across Original Medical Datasets to Fully Synthetic Images". En Deep Generative Models, 99–109. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-53767-7_10.
Texto completoNoor, Muhammad Nouman, Imran Ashraf y Muhammad Nazir. "Analysis of GAN-Based Data Augmentation for GI-Tract Disease Classification". En Advances in Deep Generative Models for Medical Artificial Intelligence, 43–64. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-46341-9_2.
Texto completoZeng, Zhi, Wei Liang, Heping Li y Shuwu Zhang. "A Novel Video Classification Method Based on Hybrid Generative/Discriminative Models". En Lecture Notes in Computer Science, 705–13. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-89689-0_74.
Texto completoShankar, Venkatesh Gauri y Dilip Singh Sisodia. "Deep Generative Adversarial Network-Based MRI Slices Reconstruction and Enhancement for Alzheimer’s Stages Classification". En Advances in Deep Generative Models for Medical Artificial Intelligence, 65–82. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-46341-9_3.
Texto completoÇetiner, Halit y Sedat Metlek. "A New CNN-Based Deep Learning Model Approach for Skin Cancer Detection and Classification". En Advances in Deep Generative Models for Medical Artificial Intelligence, 177–99. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-46341-9_7.
Texto completoLiu, Lei, Zheng Pei, Peng Chen, Zhisheng Gao, Zhihao Gan y Kang Feng. "An Effective GAN-Based Multi-classification Approach for Financial Time Series". En Proceeding of 2021 International Conference on Wireless Communications, Networking and Applications, 1100–1107. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2456-9_110.
Texto completoTrivedi, Tvisha, S. Geetha y P. Punithavathi. "A Hyperspectral Image Classification Method-Based Auxiliary Generative Adversarial Networks with Probabilistic Graph Model". En Lecture Notes in Electrical Engineering, 363–73. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-1244-2_31.
Texto completoKumar, Rahul, K. Karthik y S. Sowmya Kamath. "GAN-Based Encoder-Decoder Model for Multi-Label Diagnostic Scan Classification and Automated Radiology Report Generation". En Handbook of AI-Based Models in Healthcare and Medicine, 93–109. Boca Raton: CRC Press, 2023. http://dx.doi.org/10.1201/9781003363361-6.
Texto completoKrüger, Nina, Jan Brüning, Leonid Goubergrits, Matthias Ivantsits, Lars Walczak, Volkmar Falk, Henryk Dreger, Titus Kühne y Anja Hennemuth. "Deep Learning-Based Pulmonary Artery Surface Mesh Generation". En Statistical Atlases and Computational Models of the Heart. Regular and CMRxRecon Challenge Papers, 140–51. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-52448-6_14.
Texto completoLiu, Xinyue, Gang Yang, Yang Zhou, Yajie Yang, Weichen Huang, Dayong Ding y Jun Wu. "Fine-Grained Multi-modal Fundus Image Generation Based on Diffusion Models for Glaucoma Classification". En MultiMedia Modeling, 58–70. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-53302-0_5.
Texto completoActas de conferencias sobre el tema "Classification based on generative models"
Reilly, Ciaran, Stephen O Shaughnessy y Christina Thorpe. "Robustness of Image-Based Malware Classification Models trained with Generative Adversarial Networks". En EICC 2023: European Interdisciplinary Cybersecurity Conference. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3590777.3590792.
Texto completoGuo, Zijie, Rong Zhi, Wuqaing Zhang, Baofeng Wang, Zhijie Fang, Vitali Kaiser, Julian Wiederer y Fabian Flohr. "Generative Model based Data Augmentation for Special Person Classification". En 2020 IEEE Intelligent Vehicles Symposium (IV). IEEE, 2020. http://dx.doi.org/10.1109/iv47402.2020.9304604.
Texto completoGuo, Zijie, Rong Zhi, Wuqaing Zhang, Baofeng Wang, Zhijie Fang, Vitali Kaiser, Julian Wiederer y Fabian Flohr. "Generative Model based Data Augmentation for Special Person Classification". En 2020 IEEE Intelligent Vehicles Symposium (IV). IEEE, 2020. http://dx.doi.org/10.1109/iv47402.2020.9304604.
Texto completoBissoto, Alceu y Sandra Avila. "Improving Skin Lesion Analysis with Generative Adversarial Networks". En Conference on Graphics, Patterns and Images. Sociedade Brasileira de Computação, 2020. http://dx.doi.org/10.5753/sibgrapi.est.2020.12986.
Texto completoNik Aznan, Nik Khadijah, Amir Atapour-Abarghouei, Stephen Bonner, Jason D. Connolly, Noura Al Moubayed y Toby P. Breckon. "Simulating Brain Signals: Creating Synthetic EEG Data via Neural-Based Generative Models for Improved SSVEP Classification". En 2019 International Joint Conference on Neural Networks (IJCNN). IEEE, 2019. http://dx.doi.org/10.1109/ijcnn.2019.8852227.
Texto completoUkwuoma, Chiagoziem C., Md Belal Bin Heyat, Mahmoud Masadeh, Faijan Akhtar, Qin Zhiguang, Emmanuel Bondzie - Selby, Omar AlShorman y Fahad Alkahtani. "Image Inpainting and Classification Agent Training Based on Reinforcement Learning and Generative Models with Attention Mechanism". En 2021 International Conference on Microelectronics (ICM). IEEE, 2021. http://dx.doi.org/10.1109/icm52667.2021.9664950.
Texto completoYe, Yaping, Chu He y Zhang Zhi. "Classification of time series of SAR images based on generative model". En IGARSS 2016 - 2016 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2016. http://dx.doi.org/10.1109/igarss.2016.7729294.
Texto completoNayak, Rikin J. y Jitendra P. Chaudhari. "Generative Model for Image Classification based on Hybrid Adversarial Auto Encoder". En 2023 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC). IEEE, 2023. http://dx.doi.org/10.1109/icaaic56838.2023.10140940.
Texto completoÇelik, Mustafa y Ahmet HaydarÖrnek. "GAN-Based Data Augmentation and Anonymization for Mask Classification". En 10th International Conference on Natural Language Processing (NLP 2021). Academy and Industry Research Collaboration Center (AIRCC), 2021. http://dx.doi.org/10.5121/csit.2021.112315.
Texto completoYousif, Shermeen. "Using Language-Based and Generative Deep Learning Models for Encoding Design Intentions and Modifying Architectural Design". En 110th ACSA Annual Meeting Paper Proceedings. ACSA Press, 2022. http://dx.doi.org/10.35483/acsa.am.110.32.
Texto completoInformes sobre el tema "Classification based on generative models"
Cook, Samantha, Matthew Bigl, Sandra LeGrand, Nicholas Webb, Gayle Tyree y Ronald Treminio. Landform identification in the Chihuahuan Desert for dust source characterization applications : developing a landform reference data set. Engineer Research and Development Center (U.S.), octubre de 2022. http://dx.doi.org/10.21079/11681/45644.
Texto completoAsher, Sam, Denis Nekipelov, Paul Novosad y Stephen Ryan. Classification Trees for Heterogeneous Moment-Based Models. Cambridge, MA: National Bureau of Economic Research, diciembre de 2016. http://dx.doi.org/10.3386/w22976.
Texto completoBerney, Ernest, Naveen Ganesh, Andrew Ward, J. Newman y John Rushing. Methodology for remote assessment of pavement distresses from point cloud analysis. Engineer Research and Development Center (U.S.), abril de 2021. http://dx.doi.org/10.21079/11681/40401.
Texto completoMbani, Benson, Timm Schoening y Jens Greinert. Automated and Integrated Seafloor Classification Workflow (AI-SCW). GEOMAR, mayo de 2023. http://dx.doi.org/10.3289/sw_2_2023.
Texto completoDesa, Hazry y Muhammad Azizi Azizan. OPTIMIZING STOCKPILE MANAGEMENT THROUGH DRONE MAPPING FOR VOLUMETRIC CALCULATION. Penerbit Universiti Malaysia Perlis, 2023. http://dx.doi.org/10.58915/techrpt2023.004.
Texto completoOsadcha, Kateryna, Viacheslav Osadchyi, Serhiy Semerikov, Hanna Chemerys y Alona Chorna. The Review of the Adaptive Learning Systems for the Formation of Individual Educational Trajectory. [б. в.], noviembre de 2020. http://dx.doi.org/10.31812/123456789/4130.
Texto completoMarra de Artiñano, Ignacio, Franco Riottini Depetris y Christian Volpe Martincus. Automatic Product Classification in International Trade: Machine Learning and Large Language Models. Inter-American Development Bank, julio de 2023. http://dx.doi.org/10.18235/0005012.
Texto completoSadoune, Igor, Marcelin Joanis y Andrea Lodi. Implementing a Hierarchical Deep Learning Approach for Simulating multilevel Auction Data. CIRANO, septiembre de 2023. http://dx.doi.org/10.54932/lqog8430.
Texto completoHuang, Lei, Meng Song, Hui Shen, Huixiao Hong, Ping Gong, Deng Hong-Wen y Zhang Chaoyang. Deep learning methods for omics data imputation. Engineer Research and Development Center (U.S.), febrero de 2024. http://dx.doi.org/10.21079/11681/48221.
Texto completoKingston, A. W., A. Mort, C. Deblonde y O H Ardakani. Hydrogen sulfide (H2S) distribution in the Triassic Montney Formation of the Western Canadian Sedimentary Basin. Natural Resources Canada/CMSS/Information Management, 2022. http://dx.doi.org/10.4095/329797.
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