Literatura académica sobre el tema "Machine Learning, Artificial Intelligence, Regularization Methods"
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Artículos de revistas sobre el tema "Machine Learning, Artificial Intelligence, Regularization Methods"
Abidine, M’hamed Bilal y Belkacem Fergani. "Activity recognition from smartphone data using weighted learning methods". Intelligenza Artificiale 15, n.º 1 (28 de julio de 2021): 1–15. http://dx.doi.org/10.3233/ia-200059.
Texto completoFokkema, Marjolein, Dragos Iliescu, Samuel Greiff y Matthias Ziegler. "Machine Learning and Prediction in Psychological Assessment". European Journal of Psychological Assessment 38, n.º 3 (mayo de 2022): 165–75. http://dx.doi.org/10.1027/1015-5759/a000714.
Texto completoКабанихин, С. И. "Inverse Problems and Artificial Intelligence". Успехи кибернетики / Russian Journal of Cybernetics, n.º 3 (11 de octubre de 2021): 33–43. http://dx.doi.org/10.51790/2712-9942-2021-2-3-5.
Texto completoMohammad-Djafari, Ali. "Interaction between Model Based Signal and Image Processing, Machine Learning and Artificial Intelligence". Proceedings 33, n.º 1 (28 de noviembre de 2019): 16. http://dx.doi.org/10.3390/proceedings2019033016.
Texto completoDif, Nassima y Zakaria Elberrichi. "Efficient Regularization Framework for Histopathological Image Classification Using Convolutional Neural Networks." International Journal of Cognitive Informatics and Natural Intelligence 14, n.º 4 (octubre de 2020): 62–81. http://dx.doi.org/10.4018/ijcini.2020100104.
Texto completoLuo, Yong, Liancheng Yin, Wenchao Bai y Keming Mao. "An Appraisal of Incremental Learning Methods". Entropy 22, n.º 11 (22 de octubre de 2020): 1190. http://dx.doi.org/10.3390/e22111190.
Texto completoAlcin, Omer F., Abdulkadir Sengur, Jiang Qian y Melih C. Ince. "OMP-ELM: Orthogonal Matching Pursuit-Based Extreme Learning Machine for Regression". Journal of Intelligent Systems 24, n.º 1 (1 de marzo de 2015): 135–43. http://dx.doi.org/10.1515/jisys-2014-0095.
Texto completoHomayouni, Haleh y Eghbal G. Mansoori. "Manifold regularization ensemble clustering with many objectives using unsupervised extreme learning machines". Intelligent Data Analysis 25, n.º 4 (9 de julio de 2021): 847–62. http://dx.doi.org/10.3233/ida-205362.
Texto completoNayef, Bahera Hani, Siti Norul Huda Sheikh Abdullah, Rossilawati Sulaiman y Zaid Abdi Al Kareem Alyasseri. "VARIANTS OF NEURAL NETWORKS: A REVIEW". Malaysian Journal of Computer Science 35, n.º 2 (29 de abril de 2022): 158–78. http://dx.doi.org/10.22452/mjcs.vol35no2.5.
Texto completoCai, Yingfeng, Youguo He, Hai Wang, Xiaoqiang Sun, Long Chen y Haobin Jiang. "Pedestrian detection algorithm in traffic scene based on weakly supervised hierarchical deep model". International Journal of Advanced Robotic Systems 14, n.º 1 (14 de febrero de 2016): 172988141769231. http://dx.doi.org/10.1177/1729881417692311.
Texto completoTesis sobre el tema "Machine Learning, Artificial Intelligence, Regularization Methods"
ROSSI, ALESSANDRO. "Regularization and Learning in the temporal domain". Doctoral thesis, Università di Siena, 2017. http://hdl.handle.net/11365/1006818.
Texto completoLu, Yibiao. "Statistical methods with application to machine learning and artificial intelligence". Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/44730.
Texto completoGiuliani, Luca. "Extending the Moving Targets Method for Injecting Constraints in Machine Learning". Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/23885/.
Texto completoLe, Truc Duc. "Machine Learning Methods for 3D Object Classification and Segmentation". Thesis, University of Missouri - Columbia, 2019. http://pqdtopen.proquest.com/#viewpdf?dispub=13877153.
Texto completoObject understanding is a fundamental problem in computer vision and it has been extensively researched in recent years thanks to the availability of powerful GPUs and labelled data, especially in the context of images. However, 3D object understanding is still not on par with its 2D domain and deep learning for 3D has not been fully explored yet. In this dissertation, I work on two approaches, both of which advances the state-of-the-art results in 3D classification and segmentation.
The first approach, called MVRNN, is based multi-view paradigm. In contrast to MVCNN which does not generate consistent result across different views, by treating the multi-view images as a temporal sequence, our MVRNN correlates the features and generates coherent segmentation across different views. MVRNN demonstrated state-of-the-art performance on the Princeton Segmentation Benchmark dataset.
The second approach, called PointGrid, is a hybrid method which combines points and regular grid structure. 3D points can retain fine details but irregular, which is challenge for deep learning methods. Volumetric grid is simple and has regular structure, but does not scale well with data resolution. Our PointGrid, which is simple, allows the fine details to be consumed by normal convolutions under a coarser resolution grid. PointGrid achieved state-of-the-art performance on ModelNet40 and ShapeNet datasets in 3D classification and object part segmentation.
Michael, Christoph Cornelius. "General methods for analyzing machine learning sample complexity". W&M ScholarWorks, 1994. https://scholarworks.wm.edu/etd/1539623860.
Texto completoGao, Xi. "Graph-based Regularization in Machine Learning: Discovering Driver Modules in Biological Networks". VCU Scholars Compass, 2015. http://scholarscompass.vcu.edu/etd/3942.
Texto completoPuthiya, Parambath Shameem Ahamed. "New methods for multi-objective learning". Thesis, Compiègne, 2016. http://www.theses.fr/2016COMP2322/document.
Texto completoMulti-objective problems arise in many real world scenarios where one has to find an optimal solution considering the trade-off between different competing objectives. Typical examples of multi-objective problems arise in classification, information retrieval, dictionary learning, online learning etc. In this thesis, we study and propose algorithms for multi-objective machine learning problems. We give many interesting examples of multi-objective learning problems which are actively persuaded by the research community to motivate our work. Majority of the state of the art algorithms proposed for multi-objective learning comes under what is called “scalarization method”, an efficient algorithm for solving multi-objective optimization problems. Having motivated our work, we study two multi-objective learning tasks in detail. In the first task, we study the problem of finding the optimal classifier for multivariate performance measures. The problem is studied very actively and recent papers have proposed many algorithms in different classification settings. We study the problem as finding an optimal trade-off between different classification errors, and propose an algorithm based on cost-sensitive classification. In the second task, we study the problem of diverse ranking in information retrieval tasks, in particular recommender systems. We propose an algorithm for diverse ranking making use of the domain specific information, and formulating the problem as a submodular maximization problem for coverage maximization in a weighted similarity graph. Finally, we conclude that scalarization based algorithms works well for multi-objective learning problems. But when considering algorithms for multi-objective learning problems, scalarization need not be the “to go” approach. It is very important to consider the domain specific information and objective functions. We end this thesis by proposing some of the immediate future work, which are currently being experimented, and some of the short term future work which we plan to carry out
He, Yuesheng. "The intelligent behavior of 3D graphical avatars based on machine learning methods". HKBU Institutional Repository, 2012. https://repository.hkbu.edu.hk/etd_ra/1404.
Texto completoSirin, Volkan. "Machine Learning Methods For Opponent Modeling In Games Of Imperfect Information". Master's thesis, METU, 2012. http://etd.lib.metu.edu.tr/upload/12614630/index.pdf.
Texto completoWallis, David. "A study of machine learning and deep learning methods and their application to medical imaging". Thesis, université Paris-Saclay, 2021. http://www.theses.fr/2021UPAST057.
Texto completoWe first use Convolutional Neural Networks (CNNs) to automate mediastinal lymph node detection using FDG-PET/CT scans. We build a fully automated model to go directly from whole-body FDG-PET/CT scans to node localisation. The results show a comparable performance to an experienced physician. In the second half of the thesis we experimentally test the performance, interpretability, and stability of radiomic and CNN models on three datasets (2D brain MRI scans, 3D CT lung scans, 3D FDG-PET/CT mediastinal scans). We compare how the models improve as more data is available and examine whether there are patterns common to the different problems. We question whether current methods for model interpretation are satisfactory. We also investigate how precise segmentation affects the performance of the models. We first use Convolutional Neural Networks (CNNs) to automate mediastinal lymph node detection using FDG-PET/CT scans. We build a fully automated model to go directly from whole-body FDG-PET/CT scans to node localisation. The results show a comparable performance to an experienced physician. In the second half of the thesis we experimentally test the performance, interpretability, and stability of radiomic and CNN models on three datasets (2D brain MRI scans, 3D CT lung scans, 3D FDG-PET/CT mediastinal scans). We compare how the models improve as more data is available and examine whether there are patterns common to the different problems. We question whether current methods for model interpretation are satisfactory. We also investigate how precise segmentation affects the performance of the models
Libros sobre el tema "Machine Learning, Artificial Intelligence, Regularization Methods"
G, Carbonell Jaime, ed. Machine learning: Paradigms and methods. Cambridge, Mass: MIT Press, 1990.
Buscar texto completoSteven, Minton y Symposium on Learning Methods for Planning Systems (1991 : Stanford University), eds. Machine learning methods for planning. San Mateo, Calif: M. Kaufmann, 1993.
Buscar texto completoG, Bourbakis Nikolaos, ed. Applications of learning & planning methods. Singapore: World Scientific, 1991.
Buscar texto completoAldrich, Chris. Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods. London: Springer London, 2013.
Buscar texto completoJ, Smola Alexander, ed. Learning with kernels: Support vector machines, regularization, optimization, and beyond. Cambridge, Mass: MIT Press, 2002.
Buscar texto completoChang, Victor, Harleen Kaur y Simon James Fong, eds. Artificial Intelligence and Machine Learning Methods in COVID-19 and Related Health Diseases. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-04597-4.
Texto completoBaruque, Bruno. Fusion methods for unsupervised learning ensembles. Berlin: Springer, 2010.
Buscar texto completoKatharina, Morik, ed. Knowledge acquisition and machine learning: Theory, methods and applications / Katharina Morik ... [et al.]. London: Academic Press, 1993.
Buscar texto completoservice), SpringerLink (Online, ed. Criminal Justice Forecasts of Risk: A Machine Learning Approach. New York, NY: Springer New York, 2012.
Buscar texto completoLéon-Charles, Tranchevent, Moor Bart, Moreau Yves y SpringerLink (Online service), eds. Kernel-based Data Fusion for Machine Learning: Methods and Applications in Bioinformatics and Text Mining. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011.
Buscar texto completoCapítulos de libros sobre el tema "Machine Learning, Artificial Intelligence, Regularization Methods"
Joshi, Ameet V. "Linear Methods". En Machine Learning and Artificial Intelligence, 33–41. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-26622-6_4.
Texto completoJoshi, Ameet V. "Linear Methods". En Machine Learning and Artificial Intelligence, 45–56. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-12282-8_5.
Texto completoJovic, Alan, Dirmanto Jap, Louiza Papachristodoulou y Annelie Heuser. "Traditional Machine Learning Methods for Side-Channel Analysis". En Security and Artificial Intelligence, 25–47. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-98795-4_2.
Texto completoBaldi, Pierre. "Machine Learning Methods for Computational Proteomics and Beyond". En Advances in Artificial Intelligence, 8. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-44886-1_3.
Texto completoAnh, Nguyen Thi Ngoc, Tran Ngoc Thang y Vijender Kumar Solanki. "Machine Learning and Ensemble Methods". En Artificial Intelligence for Automated Pricing Based on Product Descriptions, 9–18. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-4702-4_2.
Texto completoTurtiainen, Hannu, Andrei Costin y Timo Hämäläinen. "Defensive Machine Learning Methods and the Cyber Defence Chain". En Artificial Intelligence and Cybersecurity, 147–63. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-15030-2_7.
Texto completoTurtiainen, Hannu, Andrei Costin, Alex Polyakov y Timo Hämäläinen. "Offensive Machine Learning Methods and the Cyber Kill Chain". En Artificial Intelligence and Cybersecurity, 125–45. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-15030-2_6.
Texto completoGhosh, Shyamasree y Rathi Dasgupta. "Introduction to Artificial Intelligence (AI) Methods in Biology". En Machine Learning in Biological Sciences, 19–27. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8881-2_2.
Texto completoCastanheira, José, Francisco Curado, Ana Tomé y Edgar Gonçalves. "Machine Learning Methods for Radar-Based People Detection and Tracking". En Progress in Artificial Intelligence, 412–23. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30241-2_35.
Texto completoIosifidis, Alexandros, Anastasios Tefas y Ioannis Pitas. "Multi-view Regularized Extreme Learning Machine for Human Action Recognition". En Artificial Intelligence: Methods and Applications, 84–94. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07064-3_7.
Texto completoActas de conferencias sobre el tema "Machine Learning, Artificial Intelligence, Regularization Methods"
Lin, Weibo, Zhu He y Mingyu Xiao. "Balanced Clustering: A Uniform Model and Fast Algorithm". En Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/414.
Texto completoLin, Jianxin, Yingce Xia, Yijun Wang, Tao Qin y Zhibo Chen. "Image-to-Image Translation with Multi-Path Consistency Regularization". En Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/413.
Texto completoLiu, Chuanjian, Yunhe Wang, Kai Han, Chunjing Xu y Chang Xu. "Learning Instance-wise Sparsity for Accelerating Deep Models". En Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/416.
Texto completoZhang, Yizhou, Guojie Song, Lun Du, Shuwen Yang y Yilun Jin. "DANE: Domain Adaptive Network Embedding". En Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/606.
Texto completoPizarro, Jorge, Byron Vásquez, Willan Steven Mendieta Molina y Remigio Hurtado. "Hepatitis predictive analysis model through deep learning using neural networks based on patient history". En 13th International Conference on Applied Human Factors and Ergonomics (AHFE 2022). AHFE International, 2022. http://dx.doi.org/10.54941/ahfe1001449.
Texto completoDeksne, Daiga. "Chat Language Normalisation using Machine Learning Methods". En Special Session on Natural Language Processing in Artificial Intelligence. SCITEPRESS - Science and Technology Publications, 2019. http://dx.doi.org/10.5220/0007693509650972.
Texto completoGARIP, Evin y Ayse Betul OKTAY. "Forecasting CO2 Emission with Machine Learning Methods". En 2018 International Conference on Artificial Intelligence and Data Processing (IDAP). IEEE, 2018. http://dx.doi.org/10.1109/idap.2018.8620767.
Texto completoLin, Zizhao y Yijiang Ma. "Machine learning methods in predicting electroencephalogram". En International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2021), editado por Lei Zhang, Siting Chen y Mahmoud AlShawabkeh. SPIE, 2021. http://dx.doi.org/10.1117/12.2626522.
Texto completoChen, Yi. "Driver fatigue detection using machine learning methods". En 2022 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA). IEEE, 2022. http://dx.doi.org/10.1109/icaica54878.2022.9844425.
Texto completoZainuddin, Nur Nadirah, Muhammad Sadiq Naim Bin Noor Azhari, Wahidah Hashim, Ammar Ahmed Alkahtani, Abdulsalam Salihu Mustafa, Gamal Alkawsi y Fuad Noman. "Malaysian Coins Recognition Using Machine Learning Methods". En 2021 2nd International Conference on Artificial Intelligence and Data Sciences (AiDAS). IEEE, 2021. http://dx.doi.org/10.1109/aidas53897.2021.9574175.
Texto completoInformes sobre el tema "Machine Learning, Artificial Intelligence, Regularization Methods"
Varastehpour, Soheil, Hamid Sharifzadeh y Iman Ardekani. A Comprehensive Review of Deep Learning Algorithms. Unitec ePress, 2021. http://dx.doi.org/10.34074/ocds.092.
Texto completoAlhasson, Haifa F. y Shuaa S. Alharbi. New Trends in image-based Diabetic Foot Ucler Diagnosis Using Machine Learning Approaches: A Systematic Review. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, noviembre de 2022. http://dx.doi.org/10.37766/inplasy2022.11.0128.
Texto completoYaroshchuk, Svitlana O., Nonna N. Shapovalova, Andrii M. Striuk, Olena H. Rybalchenko, Iryna O. Dotsenko y Svitlana V. Bilashenko. Credit scoring model for microfinance organizations. [б. в.], febrero de 2020. http://dx.doi.org/10.31812/123456789/3683.
Texto completoPerdigão, Rui A. P. Information physics and quantum space technologies for natural hazard sensing, modelling and prediction. Meteoceanics, septiembre de 2021. http://dx.doi.org/10.46337/210930.
Texto completoDaudelin, Francois, Lina Taing, Lucy Chen, Claudia Abreu Lopes, Adeniyi Francis Fagbamigbe y Hamid Mehmood. Mapping WASH-related disease risk: A review of risk concepts and methods. United Nations University Institute for Water, Environment and Health, diciembre de 2021. http://dx.doi.org/10.53328/uxuo4751.
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