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Статті в журналах з теми "Bayesian Machine Learning (BML)"
Rigueira, Xurxo, María Pazo, María Araújo, Saki Gerassis, and Elvira Bocos. "Bayesian Machine Learning and Functional Data Analysis as a Two-Fold Approach for the Study of Acid Mine Drainage Events." Water 15, no. 8 (April 15, 2023): 1553. http://dx.doi.org/10.3390/w15081553.
Повний текст джерелаMobiny, Aryan, Aditi Singh, and Hien Van Nguyen. "Risk-Aware Machine Learning Classifier for Skin Lesion Diagnosis." Journal of Clinical Medicine 8, no. 8 (August 17, 2019): 1241. http://dx.doi.org/10.3390/jcm8081241.
Повний текст джерелаOladyshkin, Sergey, Farid Mohammadi, Ilja Kroeker, and Wolfgang Nowak. "Bayesian3 Active Learning for the Gaussian Process Emulator Using Information Theory." Entropy 22, no. 8 (August 13, 2020): 890. http://dx.doi.org/10.3390/e22080890.
Повний текст джерелаZhou, Ting, Xiaohu Wen, Qi Feng, Haijiao Yu, and 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, no. 1 (December 29, 2022): 188. http://dx.doi.org/10.3390/rs15010188.
Повний текст джерелаKim, Sungwon, Meysam Alizamir, Nam Won Kim, and Ozgur Kisi. "Bayesian Model Averaging: A Unique Model Enhancing Forecasting Accuracy for Daily Streamflow Based on Different Antecedent Time Series." Sustainability 12, no. 22 (November 21, 2020): 9720. http://dx.doi.org/10.3390/su12229720.
Повний текст джерелаNajafi, Mohammad Reza, Zahra Kavianpour, Banafsheh Najafi, Mohammad Reza Kavianpour, and Hamid Moradkhani. "Air demand in gated tunnels – a Bayesian approach to merge various predictions." Journal of Hydroinformatics 14, no. 1 (April 23, 2011): 152–66. http://dx.doi.org/10.2166/hydro.2011.108.
Повний текст джерелаXu, Ren, Nengcheng Chen, Yumin Chen, and Zeqiang Chen. "Downscaling and Projection of Multi-CMIP5 Precipitation Using Machine Learning Methods in the Upper Han River Basin." Advances in Meteorology 2020 (March 9, 2020): 1–17. http://dx.doi.org/10.1155/2020/8680436.
Повний текст джерелаShu, Meiyan, Shuaipeng Fei, Bingyu Zhang, Xiaohong Yang, Yan Guo, Baoguo Li, and Yuntao Ma. "Application of UAV Multisensor Data and Ensemble Approach for High-Throughput Estimation of Maize Phenotyping Traits." Plant Phenomics 2022 (August 28, 2022): 1–17. http://dx.doi.org/10.34133/2022/9802585.
Повний текст джерелаQuadeer, Ahmed A., Matthew R. McKay, John P. Barton, and Raymond H. Y. Louie. "MPF–BML: a standalone GUI-based package for maximum entropy model inference." Bioinformatics 36, no. 7 (December 18, 2019): 2278–79. http://dx.doi.org/10.1093/bioinformatics/btz925.
Повний текст джерелаSoria-Olivas, E., J. Gomez-Sanchis, J. D. Martin, J. Vila-Frances, M. Martinez, J. R. Magdalena, and A. J. Serrano. "BELM: Bayesian Extreme Learning Machine." IEEE Transactions on Neural Networks 22, no. 3 (March 2011): 505–9. http://dx.doi.org/10.1109/tnn.2010.2103956.
Повний текст джерелаДисертації з теми "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.
Повний текст джерелаHigson, Edward John. "Bayesian methods and machine learning in astrophysics." Thesis, University of Cambridge, 2019. https://www.repository.cam.ac.uk/handle/1810/289728.
Повний текст джерелаMenke, Joshua E. "Improving machine learning through oracle learning /." Diss., CLICK HERE for online access, 2007. http://contentdm.lib.byu.edu/ETD/image/etd1726.pdf.
Повний текст джерелаMenke, Joshua Ephraim. "Improving Machine Learning Through Oracle Learning." BYU ScholarsArchive, 2007. https://scholarsarchive.byu.edu/etd/843.
Повний текст джерелаHuszár, 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.
Повний текст джерелаRoychowdhury, Anirban. "Robust and Scalable Algorithms for Bayesian Nonparametric Machine Learning." The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1511901271093727.
Повний текст джерелаYu, 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.
Повний текст джерелаShahriari, Bobak. "Practical Bayesian optimization with application to tuning machine learning algorithms." Thesis, University of British Columbia, 2016. http://hdl.handle.net/2429/59104.
Повний текст джерелаScience, 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.
Повний текст джерелаScalabrin, Maria. "Bayesian Learning Strategies in Wireless Networks." Doctoral thesis, Università degli studi di Padova, 2018. http://hdl.handle.net/11577/3424931.
Повний текст джерелаQuesta 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.
Книги з теми "Bayesian Machine Learning (BML)"
Barber, David. Bayesian reasoning and machine learning. Cambridge: Cambridge University Press, 2011.
Знайти повний текст джерелаResearch Institute for Advanced Computer Science (U.S.), ed. Bayesian learning. [Moffett Field, Calif.]: Research Institute for Advanced Computer Science, NASA Ames Research Center, 1989.
Знайти повний текст джерелаLearning Bayesian networks. Harlow: Prentice Hall, 2003.
Знайти повний текст джерелаNeapolitan, Richard E. Learning Bayesian networks. Upper Saddle River, NJ: Pearson Prentice Hall, 2004.
Знайти повний текст джерелаNeal, Radford M. Bayesian learning for neural networks. New York: Springer, 1996.
Знайти повний текст джерелаNeal, Radford M. Bayesian learning for neural networks. Toronto: University of Toronto, Dept. of Computer Science, 1995.
Знайти повний текст джерелаHemachandran, K., Shubham Tayal, Preetha Mary George, Parveen Singla, and 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.
Повний текст джерелаCheng, Lei, Zhongtao Chen, and 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.
Повний текст джерелаMACKAY, DAVID J. C. Information Theory, Inference & Learning Algorithms. Cambridge, UK: Cambridge University Press, 2003.
Знайти повний текст джерелаE, Nicholson Ann, ed. Bayesian artificial intelligence. Boca Raton, Fla: Chapman & Hall/CRC, 2004.
Знайти повний текст джерелаЧастини книг з теми "Bayesian Machine Learning (BML)"
van Oijen, Marcel. "Machine Learning." In Bayesian Compendium, 141–49. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-55897-0_20.
Повний текст джерелаCleophas, Ton J., and Aeilko H. Zwinderman. "Bayesian Networks." In Machine Learning in Medicine, 163–70. Dordrecht: Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-94-007-6886-4_16.
Повний текст джерелаWebb, Geoffrey I., Eamonn Keogh, Risto Miikkulainen, Risto Miikkulainen, and Michele Sebag. "Nonparametric Bayesian." In Encyclopedia of Machine Learning, 722. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_596.
Повний текст джерелаMunro, Paul, Hannu Toivonen, Geoffrey I. Webb, Wray Buntine, Peter Orbanz, Yee Whye Teh, Pascal Poupart, et al. "Bayesian Methods." In Encyclopedia of Machine Learning, 75–81. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_63.
Повний текст джерелаMunro, Paul, Hannu Toivonen, Geoffrey I. Webb, Wray Buntine, Peter Orbanz, Yee Whye Teh, Pascal Poupart, et al. "Bayesian Network." In Encyclopedia of Machine Learning, 81. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_65.
Повний текст джерелаWebb, Geoffrey I., Claude Sammut, Claudia Perlich, Tamás Horváth, Stefan Wrobel, Kevin B. Korb, William Stafford Noble, et al. "Learning Bayesian Networks." In Encyclopedia of Machine Learning, 577. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_445.
Повний текст джерелаMunro, Paul, Hannu Toivonen, Geoffrey I. Webb, Wray Buntine, Peter Orbanz, Yee Whye Teh, Pascal Poupart, et al. "Bayesian Reinforcement Learning." In Encyclopedia of Machine Learning, 90–93. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_67.
Повний текст джерелаWebb, Geoffrey I., Johannes Fürnkranz, Johannes Fürnkranz, Johannes Fürnkranz, Geoffrey Hinton, Claude Sammut, Joerg Sander, et al. "Dynamic Bayesian Network." In Encyclopedia of Machine Learning, 298. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_234.
Повний текст джерелаMunro, Paul, Hannu Toivonen, Geoffrey I. Webb, Wray Buntine, Peter Orbanz, Yee Whye Teh, Pascal Poupart, et al. "Bayesian Model Averaging." In Encyclopedia of Machine Learning, 81. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_64.
Повний текст джерелаMunro, Paul, Hannu Toivonen, Geoffrey I. Webb, Wray Buntine, Peter Orbanz, Yee Whye Teh, Pascal Poupart, et al. "Bayesian Nonparametric Models." In Encyclopedia of Machine Learning, 81–89. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_66.
Повний текст джерелаТези доповідей конференцій з теми "Bayesian Machine Learning (BML)"
Bayerl, Mathias, Pascale Neff, Torsten Clemens, Martin Sieberer, Barbara Stummer, and Andras Zamolyi. "Accelerating Mature Field EOR Evaluation Using Machine Learning Uncertainty Workflows Integrating Subsurface And Economics." In Abu Dhabi International Petroleum Exhibition & Conference. SPE, 2021. http://dx.doi.org/10.2118/208194-ms.
Повний текст джерелаMoumen, Aniss, Imane El Bakkouri, Hamza Kadimi, Abir Zahi, Ihsane Sardi, Mohammed Saad Tebaa, Ziyad Bousserrhine, and Hanae Baraka. "Machine Learning for Students Employability Prediction." In INTERNATIONAL CONFERENCE ON BIG DATA, MODELLING AND MACHINE LEARNING (BML'21). SCITEPRESS - Science and Technology Publications, 2021. http://dx.doi.org/10.5220/0010732400003101.
Повний текст джерелаChentoufi, Oumaima, and Khalid Chougdali. "Intrusion Detection Systems based on Machine Learning." In INTERNATIONAL CONFERENCE ON BIG DATA, MODELLING AND MACHINE LEARNING (BML'21). SCITEPRESS - Science and Technology Publications, 2021. http://dx.doi.org/10.5220/0010734300003101.
Повний текст джерелаZine-dine, Iliass, Jamal Riffi, Khalid El Fazazi, Mohamed Adnane Mahraz, and Hamid Tairi. "Brain Tumor Classification using Machine and Transfer Learning." In INTERNATIONAL CONFERENCE ON BIG DATA, MODELLING AND MACHINE LEARNING (BML'21). SCITEPRESS - Science and Technology Publications, 2021. http://dx.doi.org/10.5220/0010762800003101.
Повний текст джерелаQarmiche, Noura, Mehdi Chrifi Alaoui, Nada Otmani, Samira El Fakir, Nabil Tachfouti, Hind Bourkhime, Mohammed Omari, Karima El Rhazi, and Nour El Houda Chaoui. "Machine Learning for Colorectal Cancer Risk Prediction: Systematic Review." In INTERNATIONAL CONFERENCE ON BIG DATA, MODELLING AND MACHINE LEARNING (BML'21). SCITEPRESS - Science and Technology Publications, 2021. http://dx.doi.org/10.5220/0010738100003101.
Повний текст джерелаNaidenova, Xenia, and Sergey Kurbatov. "Self-supervised Learning in Symbolic Classification." In INTERNATIONAL CONFERENCE ON BIG DATA, MODELLING AND MACHINE LEARNING (BML'21). SCITEPRESS - Science and Technology Publications, 2021. http://dx.doi.org/10.5220/0010732700003101.
Повний текст джерелаBoussadia, Nawres, and Olfa Belkahla Driss. "Machine Learning for Dynamic Job Shop Scheduling Problem: Literature Review." In INTERNATIONAL CONFERENCE ON BIG DATA, MODELLING AND MACHINE LEARNING (BML'21). SCITEPRESS - Science and Technology Publications, 2021. http://dx.doi.org/10.5220/0010736200003101.
Повний текст джерелаMboutayeb, Saad, Aicha Majda, and Nikola S. Nikolov. "Multilingual Sentiment Analysis: A Deep Learning Approach." In INTERNATIONAL CONFERENCE ON BIG DATA, MODELLING AND MACHINE LEARNING (BML'21). SCITEPRESS - Science and Technology Publications, 2021. http://dx.doi.org/10.5220/0010727700003101.
Повний текст джерелаHakkal, Soukaina, and Ayoub Ait Lahcen. "An Overview of Adaptive Learning Fee-based Platforms." In INTERNATIONAL CONFERENCE ON BIG DATA, MODELLING AND MACHINE LEARNING (BML'21). SCITEPRESS - Science and Technology Publications, 2021. http://dx.doi.org/10.5220/0010731400003101.
Повний текст джерелаChadi, Mohamed-Amine, and Hajar Mousannif. "Inverse Reinforcement Learning for Healthcare Applications: A Survey." In INTERNATIONAL CONFERENCE ON BIG DATA, MODELLING AND MACHINE LEARNING (BML'21). SCITEPRESS - Science and Technology Publications, 2021. http://dx.doi.org/10.5220/0010729200003101.
Повний текст джерелаЗвіти організацій з теми "Bayesian Machine Learning (BML)"
Qi, Fei, Zhaohui Xia, Gaoyang Tang, Hang Yang, Yu Song, Guangrui Qian, Xiong An, Chunhuan Lin, and Guangming Shi. A Graph-based Evolutionary Algorithm for Automated Machine Learning. Web of Open Science, December 2020. http://dx.doi.org/10.37686/ser.v1i2.77.
Повний текст джерелаHauzenberger, Niko, Florian Huber, Gary Koop, and James Mitchell. Bayesian modeling of time-varying parameters using regression trees. Federal Reserve Bank of Cleveland, January 2023. http://dx.doi.org/10.26509/frbc-wp-202305.
Повний текст джерелаGungor, Osman, Imad Al-Qadi, and Navneet Garg. Pavement Data Analytics for Collected Sensor Data. Illinois Center for Transportation, October 2021. http://dx.doi.org/10.36501/0197-9191/21-034.
Повний текст джерела