Literatura académica sobre el tema "Bayesian Machine Learning (BML)"
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Artículos de revistas sobre el tema "Bayesian Machine Learning (BML)"
Rigueira, Xurxo, María Pazo, María Araújo, Saki Gerassis y Elvira Bocos. "Bayesian Machine Learning and Functional Data Analysis as a Two-Fold Approach for the Study of Acid Mine Drainage Events". Water 15, n.º 8 (15 de abril de 2023): 1553. http://dx.doi.org/10.3390/w15081553.
Texto completoMobiny, Aryan, Aditi Singh y Hien Van Nguyen. "Risk-Aware Machine Learning Classifier for Skin Lesion Diagnosis". Journal of Clinical Medicine 8, n.º 8 (17 de agosto de 2019): 1241. http://dx.doi.org/10.3390/jcm8081241.
Texto completoOladyshkin, Sergey, Farid Mohammadi, Ilja Kroeker y Wolfgang Nowak. "Bayesian3 Active Learning for the Gaussian Process Emulator Using Information Theory". Entropy 22, n.º 8 (13 de agosto de 2020): 890. http://dx.doi.org/10.3390/e22080890.
Texto completoZhou, Ting, Xiaohu Wen, Qi Feng, Haijiao Yu y Haiyang Xi. "Bayesian Model Averaging Ensemble Approach for Multi-Time-Ahead Groundwater Level Prediction Combining the GRACE, GLEAM, and GLDAS Data in Arid Areas". Remote Sensing 15, n.º 1 (29 de diciembre de 2022): 188. http://dx.doi.org/10.3390/rs15010188.
Texto completoKim, Sungwon, Meysam Alizamir, Nam Won Kim y Ozgur Kisi. "Bayesian Model Averaging: A Unique Model Enhancing Forecasting Accuracy for Daily Streamflow Based on Different Antecedent Time Series". Sustainability 12, n.º 22 (21 de noviembre de 2020): 9720. http://dx.doi.org/10.3390/su12229720.
Texto completoNajafi, Mohammad Reza, Zahra Kavianpour, Banafsheh Najafi, Mohammad Reza Kavianpour y Hamid Moradkhani. "Air demand in gated tunnels – a Bayesian approach to merge various predictions". Journal of Hydroinformatics 14, n.º 1 (23 de abril de 2011): 152–66. http://dx.doi.org/10.2166/hydro.2011.108.
Texto completoXu, Ren, Nengcheng Chen, Yumin Chen y Zeqiang Chen. "Downscaling and Projection of Multi-CMIP5 Precipitation Using Machine Learning Methods in the Upper Han River Basin". Advances in Meteorology 2020 (9 de marzo de 2020): 1–17. http://dx.doi.org/10.1155/2020/8680436.
Texto completoShu, Meiyan, Shuaipeng Fei, Bingyu Zhang, Xiaohong Yang, Yan Guo, Baoguo Li y Yuntao Ma. "Application of UAV Multisensor Data and Ensemble Approach for High-Throughput Estimation of Maize Phenotyping Traits". Plant Phenomics 2022 (28 de agosto de 2022): 1–17. http://dx.doi.org/10.34133/2022/9802585.
Texto completoQuadeer, Ahmed A., Matthew R. McKay, John P. Barton y Raymond H. Y. Louie. "MPF–BML: a standalone GUI-based package for maximum entropy model inference". Bioinformatics 36, n.º 7 (18 de diciembre de 2019): 2278–79. http://dx.doi.org/10.1093/bioinformatics/btz925.
Texto completoSoria-Olivas, E., J. Gomez-Sanchis, J. D. Martin, J. Vila-Frances, M. Martinez, J. R. Magdalena y A. J. Serrano. "BELM: Bayesian Extreme Learning Machine". IEEE Transactions on Neural Networks 22, n.º 3 (marzo de 2011): 505–9. http://dx.doi.org/10.1109/tnn.2010.2103956.
Texto completoTesis sobre el tema "Bayesian Machine Learning (BML)"
Habli, Nada. "Nonparametric Bayesian Modelling in Machine Learning". Thesis, Université d'Ottawa / University of Ottawa, 2016. http://hdl.handle.net/10393/34267.
Texto completoHigson, Edward John. "Bayesian methods and machine learning in astrophysics". Thesis, University of Cambridge, 2019. https://www.repository.cam.ac.uk/handle/1810/289728.
Texto completoMenke, Joshua E. "Improving machine learning through oracle learning /". Diss., CLICK HERE for online access, 2007. http://contentdm.lib.byu.edu/ETD/image/etd1726.pdf.
Texto completoMenke, Joshua Ephraim. "Improving Machine Learning Through Oracle Learning". BYU ScholarsArchive, 2007. https://scholarsarchive.byu.edu/etd/843.
Texto completoHuszár, Ferenc. "Scoring rules, divergences and information in Bayesian machine learning". Thesis, University of Cambridge, 2013. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.648333.
Texto completoRoychowdhury, Anirban. "Robust and Scalable Algorithms for Bayesian Nonparametric Machine Learning". The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1511901271093727.
Texto completoYu, Shen. "A Bayesian machine learning system for recognizing group behaviour". Thesis, McGill University, 2009. http://digitool.Library.McGill.CA:8881/R/?func=dbin-jump-full&object_id=32565.
Texto completoShahriari, Bobak. "Practical Bayesian optimization with application to tuning machine learning algorithms". Thesis, University of British Columbia, 2016. http://hdl.handle.net/2429/59104.
Texto completoScience, Faculty of
Computer Science, Department of
Graduate
Sampson, Oliver [Verfasser]. "Widened Machine Learning with Application to Bayesian Networks / Oliver Sampson". Konstanz : KOPS Universität Konstanz, 2020. http://d-nb.info/1209055597/34.
Texto completoScalabrin, Maria. "Bayesian Learning Strategies in Wireless Networks". Doctoral thesis, Università degli studi di Padova, 2018. http://hdl.handle.net/11577/3424931.
Texto completoQuesta tesi raccoglie i lavori di ricerca svolti durante il mio percorso di dottorato, il cui filo conduttore è dato dal Bayesian reasoning con applicazioni in reti wireless. Il contributo fondamentale dato dal Bayesian reasoning sta nel fare deduzioni: ragionare riguardo a quello che non conosciamo, dato quello che conosciamo. Nel fare deduzioni riguardo alla natura delle cose, impariamo nuove caratteristiche proprie dell’ambiente in cui l’agente fa esperienza, e questo è ciò che ci permette di fare uso dell’informazione acquisita, adattandoci a nuove condizioni. Nel momento in cui facciamo uso dell’informazione acquisita, la nostra convinzione (belief) riguardo allo stato dell’ambiente cambia in modo tale da riflettere la nostra nuova conoscenza. Questa tesi tratta degli aspetti probabilistici nel processare l’informazione con applicazioni nei seguenti ambiti di ricerca: Machine learning based network analysis using millimeter-wave narrow-band energy traces; Bayesian forecasting and anomaly detection in vehicular monitoring networks; Online power management strategies for energy harvesting mobile networks; Beam-training and data transmission optimization in millimeter-wave vehicular networks. In questi lavori di ricerca studiamo aspetti di riconoscimento di pattern in dati reali attraverso metodi di supervised/unsupervised learning (classification, forecasting and anomaly detection, multi-step ahead prediction via kernel methods). Infine, presentiamo il contesto matematico dei Markov Decision Processes (MDPs), il quale sta anche alla base del reinforcement learning, dove Partially Observable MDPs utilizzano il concetto probabilistico di convinzione (belief) al fine di prendere decisoni riguardo allo stato dell’ambiente in millimeter-wave vehicular networks. Lo scopo di questa tesi è di investigare il considerevole potenziale nel fare deduzioni, andando a dettagliare il contesto matematico e come il modello probabilistico dato dal Bayesian reasoning si possa adattare agevolmente a vari ambiti di ricerca con applicazioni in reti wireless.
Libros sobre el tema "Bayesian Machine Learning (BML)"
Barber, David. Bayesian reasoning and machine learning. Cambridge: Cambridge University Press, 2011.
Buscar texto completoResearch Institute for Advanced Computer Science (U.S.), ed. Bayesian learning. [Moffett Field, Calif.]: Research Institute for Advanced Computer Science, NASA Ames Research Center, 1989.
Buscar texto completoLearning Bayesian networks. Harlow: Prentice Hall, 2003.
Buscar texto completoNeapolitan, Richard E. Learning Bayesian networks. Upper Saddle River, NJ: Pearson Prentice Hall, 2004.
Buscar texto completoNeal, Radford M. Bayesian learning for neural networks. New York: Springer, 1996.
Buscar texto completoNeal, Radford M. Bayesian learning for neural networks. Toronto: University of Toronto, Dept. of Computer Science, 1995.
Buscar texto completoHemachandran, K., Shubham Tayal, Preetha Mary George, Parveen Singla y Utku Kose. Bayesian Reasoning and Gaussian Processes for Machine Learning Applications. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003164265.
Texto completoCheng, Lei, Zhongtao Chen y Yik-Chung Wu. Bayesian Tensor Decomposition for Signal Processing and Machine Learning. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-22438-6.
Texto completoMACKAY, DAVID J. C. Information Theory, Inference & Learning Algorithms. Cambridge, UK: Cambridge University Press, 2003.
Buscar texto completoE, Nicholson Ann, ed. Bayesian artificial intelligence. Boca Raton, Fla: Chapman & Hall/CRC, 2004.
Buscar texto completoCapítulos de libros sobre el tema "Bayesian Machine Learning (BML)"
van Oijen, Marcel. "Machine Learning". En Bayesian Compendium, 141–49. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-55897-0_20.
Texto completoCleophas, Ton J. y Aeilko H. Zwinderman. "Bayesian Networks". En Machine Learning in Medicine, 163–70. Dordrecht: Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-94-007-6886-4_16.
Texto completoWebb, Geoffrey I., Eamonn Keogh, Risto Miikkulainen, Risto Miikkulainen y Michele Sebag. "Nonparametric Bayesian". En Encyclopedia of Machine Learning, 722. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_596.
Texto completoMunro, Paul, Hannu Toivonen, Geoffrey I. Webb, Wray Buntine, Peter Orbanz, Yee Whye Teh, Pascal Poupart et al. "Bayesian Methods". En Encyclopedia of Machine Learning, 75–81. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_63.
Texto completoMunro, Paul, Hannu Toivonen, Geoffrey I. Webb, Wray Buntine, Peter Orbanz, Yee Whye Teh, Pascal Poupart et al. "Bayesian Network". En Encyclopedia of Machine Learning, 81. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_65.
Texto completoWebb, Geoffrey I., Claude Sammut, Claudia Perlich, Tamás Horváth, Stefan Wrobel, Kevin B. Korb, William Stafford Noble et al. "Learning Bayesian Networks". En Encyclopedia of Machine Learning, 577. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_445.
Texto completoMunro, Paul, Hannu Toivonen, Geoffrey I. Webb, Wray Buntine, Peter Orbanz, Yee Whye Teh, Pascal Poupart et al. "Bayesian Reinforcement Learning". En Encyclopedia of Machine Learning, 90–93. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_67.
Texto completoWebb, Geoffrey I., Johannes Fürnkranz, Johannes Fürnkranz, Johannes Fürnkranz, Geoffrey Hinton, Claude Sammut, Joerg Sander et al. "Dynamic Bayesian Network". En Encyclopedia of Machine Learning, 298. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_234.
Texto completoMunro, Paul, Hannu Toivonen, Geoffrey I. Webb, Wray Buntine, Peter Orbanz, Yee Whye Teh, Pascal Poupart et al. "Bayesian Model Averaging". En Encyclopedia of Machine Learning, 81. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_64.
Texto completoMunro, Paul, Hannu Toivonen, Geoffrey I. Webb, Wray Buntine, Peter Orbanz, Yee Whye Teh, Pascal Poupart et al. "Bayesian Nonparametric Models". En Encyclopedia of Machine Learning, 81–89. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_66.
Texto completoActas de conferencias sobre el tema "Bayesian Machine Learning (BML)"
Bayerl, Mathias, Pascale Neff, Torsten Clemens, Martin Sieberer, Barbara Stummer y Andras Zamolyi. "Accelerating Mature Field EOR Evaluation Using Machine Learning Uncertainty Workflows Integrating Subsurface And Economics". En Abu Dhabi International Petroleum Exhibition & Conference. SPE, 2021. http://dx.doi.org/10.2118/208194-ms.
Texto completoMoumen, Aniss, Imane El Bakkouri, Hamza Kadimi, Abir Zahi, Ihsane Sardi, Mohammed Saad Tebaa, Ziyad Bousserrhine y Hanae Baraka. "Machine Learning for Students Employability Prediction". En INTERNATIONAL CONFERENCE ON BIG DATA, MODELLING AND MACHINE LEARNING (BML'21). SCITEPRESS - Science and Technology Publications, 2021. http://dx.doi.org/10.5220/0010732400003101.
Texto completoChentoufi, Oumaima y Khalid Chougdali. "Intrusion Detection Systems based on Machine Learning". En INTERNATIONAL CONFERENCE ON BIG DATA, MODELLING AND MACHINE LEARNING (BML'21). SCITEPRESS - Science and Technology Publications, 2021. http://dx.doi.org/10.5220/0010734300003101.
Texto completoZine-dine, Iliass, Jamal Riffi, Khalid El Fazazi, Mohamed Adnane Mahraz y Hamid Tairi. "Brain Tumor Classification using Machine and Transfer Learning". En INTERNATIONAL CONFERENCE ON BIG DATA, MODELLING AND MACHINE LEARNING (BML'21). SCITEPRESS - Science and Technology Publications, 2021. http://dx.doi.org/10.5220/0010762800003101.
Texto completoQarmiche, Noura, Mehdi Chrifi Alaoui, Nada Otmani, Samira El Fakir, Nabil Tachfouti, Hind Bourkhime, Mohammed Omari, Karima El Rhazi y Nour El Houda Chaoui. "Machine Learning for Colorectal Cancer Risk Prediction: Systematic Review". En INTERNATIONAL CONFERENCE ON BIG DATA, MODELLING AND MACHINE LEARNING (BML'21). SCITEPRESS - Science and Technology Publications, 2021. http://dx.doi.org/10.5220/0010738100003101.
Texto completoNaidenova, Xenia y Sergey Kurbatov. "Self-supervised Learning in Symbolic Classification". En INTERNATIONAL CONFERENCE ON BIG DATA, MODELLING AND MACHINE LEARNING (BML'21). SCITEPRESS - Science and Technology Publications, 2021. http://dx.doi.org/10.5220/0010732700003101.
Texto completoBoussadia, Nawres y Olfa Belkahla Driss. "Machine Learning for Dynamic Job Shop Scheduling Problem: Literature Review". En INTERNATIONAL CONFERENCE ON BIG DATA, MODELLING AND MACHINE LEARNING (BML'21). SCITEPRESS - Science and Technology Publications, 2021. http://dx.doi.org/10.5220/0010736200003101.
Texto completoMboutayeb, Saad, Aicha Majda y Nikola S. Nikolov. "Multilingual Sentiment Analysis: A Deep Learning Approach". En INTERNATIONAL CONFERENCE ON BIG DATA, MODELLING AND MACHINE LEARNING (BML'21). SCITEPRESS - Science and Technology Publications, 2021. http://dx.doi.org/10.5220/0010727700003101.
Texto completoHakkal, Soukaina y Ayoub Ait Lahcen. "An Overview of Adaptive Learning Fee-based Platforms". En INTERNATIONAL CONFERENCE ON BIG DATA, MODELLING AND MACHINE LEARNING (BML'21). SCITEPRESS - Science and Technology Publications, 2021. http://dx.doi.org/10.5220/0010731400003101.
Texto completoChadi, Mohamed-Amine y Hajar Mousannif. "Inverse Reinforcement Learning for Healthcare Applications: A Survey". En INTERNATIONAL CONFERENCE ON BIG DATA, MODELLING AND MACHINE LEARNING (BML'21). SCITEPRESS - Science and Technology Publications, 2021. http://dx.doi.org/10.5220/0010729200003101.
Texto completoInformes sobre el tema "Bayesian Machine Learning (BML)"
Qi, Fei, Zhaohui Xia, Gaoyang Tang, Hang Yang, Yu Song, Guangrui Qian, Xiong An, Chunhuan Lin y Guangming Shi. A Graph-based Evolutionary Algorithm for Automated Machine Learning. Web of Open Science, diciembre de 2020. http://dx.doi.org/10.37686/ser.v1i2.77.
Texto completoHauzenberger, Niko, Florian Huber, Gary Koop y James Mitchell. Bayesian modeling of time-varying parameters using regression trees. Federal Reserve Bank of Cleveland, enero de 2023. http://dx.doi.org/10.26509/frbc-wp-202305.
Texto completoGungor, Osman, Imad Al-Qadi y Navneet Garg. Pavement Data Analytics for Collected Sensor Data. Illinois Center for Transportation, octubre de 2021. http://dx.doi.org/10.36501/0197-9191/21-034.
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