Relevant bibliographies by topics / Methods of machine learning

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Methods of machine learning'

Author: Grafiati
Published: 28 May 2022
Last updated: 31 July 2025

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Journal articles on the topic "Methods of machine learning"

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Mubarakova,, S. R., S. T. Amanzholova,, and R. K. Uskenbayeva,. "USING MACHINE LEARNING METHODS IN CYBERSECURITY." Eurasian Journal of Mathematical and Computer Applications 10, no. 1 (2022): 69–78. http://dx.doi.org/10.32523/2306-6172-2022-10-1-69-78.

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Abstract Cybersecurity is an ever-changing field, with advances in technology that open up new opportunities for cyberattacks. In addition, even though serious secu- rity breaches are often reported, small organizations still have to worry about security breaches as they can often be the target of viruses and phishing. This is why it is so important to ensure the privacy of your user profile in cyberspace. The past few years have seen a rise in machine learning algorithms that address major cybersecu- rity issues such as intrusion detection systems (IDS), detection of new modifications of know
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Akgül, İsmail, and Yıldız Aydın. "OBJECT RECOGNITION WITH DEEP LEARNING AND MACHINE LEARNING METHODS." NWSA Academic Journals 17, no. 4 (2022): 54–61. http://dx.doi.org/10.12739/nwsa.2022.17.4.2a0189.

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Mishra, Dipanshu, Shrikant Mani Tripathi, Akash Chaurasia, and Pawan Kumar Chaurasia. "A Review on Ensemble Learning Methods: Machine Learning Approach." International Journal of Research Publication and Reviews 6, no. 2 (2025): 3795–803. https://doi.org/10.55248/gengpi.6.0225.0971.

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Tashev, Sarvar Norboboyevich. "DYNAMIC PACKET FILTERING USING MACHINE LEARNING METHODS." American Journal of Applied Science and Technology 4, no. 10 (2024): 69–79. http://dx.doi.org/10.37547/ajast/volume04issue10-11.

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With the emergence of the Internet, cyber-attacks and threats have become significant issues. Traditional manual network monitoring and rule-based packet filtering methods have become labor-intensive and less effective in combating attacks. Filtering packets based solely on payload and pattern matching is also inefficient. There is a need for a dynamic model capable of learning packet filtering rules. This article proposes a packet filtering model using Neural Networks. After developing the model classified with training and validation data, it can be utilized to support dynamic packet filteri
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Aschepkov, Valeriy. "METHODS OF MACHINE LEARNING IN MODERN METROLOGY." Measuring Equipment and Metrology 85 (2024): 57–60. http://dx.doi.org/10.23939/istcmtm2024.01.057.

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In the modern world of scientific and technological progress, the requirements for the accuracy and reliability of measurements are becoming increasingly stringent. The rapid development of machine learning (ML) methods opens up perspectives for improving metrological processes and enhancing the quality of measurements. This article explores the potential application of ML methods in metrology, outlining the main types of ML models in automatic instrument calibration, analysis, and prediction of data. Attention is paid to the development of hybrid approaches that combine ML methods with tradit
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Dauitbayeva, A. O. "MODERN METHODS OF MACHINE LEARNING AND ANALYTICS." ТЕХНИКА ҒЫЛЫМДАРЫ ЖӘНЕ ТЕХНОЛОГИЯ 7, no. 3 (2024): 12–19. https://doi.org/10.52081/tst.2024.v03.i7.039.

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The article proposes algorithms for processing big data to optimize business processes. The methods of data integration, distributed computing and machine learning for analysis and forecasting are considered. Testing on business cases has shown cost reduction and increased accuracy of solutions, confirming the practical value of the developed approaches. In the modern world, the volume of data is growing exponentially, thanks to the development of technology and the ubiquity of digital devices. Petabytes of information are generated daily: This is data from social networks, electronic devices,
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Bzdok, Danilo, Martin Krzywinski, and Naomi Altman. "Machine learning: supervised methods." Nature Methods 15, no. 1 (2018): 5–6. http://dx.doi.org/10.1038/nmeth.4551.

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Turčaník, Michal. "Network User Behaviour Analysis by Machine Learning Methods." Information & Security: An International Journal 50 (2021): 66–78. http://dx.doi.org/10.11610/isij.5014.

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Deviatko, Anna. "Evolution of Automated Testing Methods Using Machine Learning." American Journal of Engineering and Technology 07, no. 05 (2025): 88–100. https://doi.org/10.37547/tajet/volume07issue05-07.

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program testing is crucial for guaranteeing program dependability, but it has historically included a lot of manual labor, which restricts coverage and raises expenses. By creating and selecting test cases, anticipating defect-prone locations, and evaluating test results, machine learning (ML)-driven testing approaches automate and improve traditional software testing. This study examines the development of these techniques. Significant enhancements are provided by ML-driven techniques, such as early fault detection, shorter testing times, and increased test coverage. The paper offers a thorou
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Olajide, Olajide Blessing, Soneye Olufemi Sobowale, Ogunniyi Olufunke Kemi, et al. "Diagnosing Malaria and Jaundice Using Selected Machine Learning Methods." International Journal of Research Publication and Reviews 5, no. 11 (2024): 3084–93. http://dx.doi.org/10.55248/gengpi.5.1124.3256.

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Dissertations / Theses on the topic "Methods of machine learning"

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Mauricio, Palacio Sebastián. "Machine-Learning Applied Methods." Doctoral thesis, Universitat de Barcelona, 2020. http://hdl.handle.net/10803/669286.

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The presented discourse followed several topics where every new chapter introduced an economic prediction problem and showed how traditional approaches can be complemented with new techniques like machine learning and deep learning. These powerful tools combined with principles of economic theory is highly increasing the scope for empiricists. Chapter 3 addressed this discussion. By progressively moving from Ordinary Least Squares, Penalized Linear Regressions and Binary Trees to advanced ensemble trees. Results showed that ML algorithms significantly outperform statistical models in terms of
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VELLOSO, SUSANA ROSICH SOARES. "SQLLOMINING: FINDING LEARNING OBJECTS USING MACHINE LEARNING METHODS." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2007. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=10970@1.

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COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR<br>Objetos de Aprendizagem ou Learning Objects (LOs) são porções de material didático tais como textos que podem ser reutilizados na composição de outros objetos maiores (aulas ou cursos). Um dos problemas da reutilização de LOs é descobri-los em seus contextos ou documentos texto originais tais como livros, e artigos. Visando a obtenção de LOs, este trabalho apresenta um processo que parte da extração, tratamento e carga de uma base de dados textual e em seguida, baseando-se em técnicas de aprendizado de máquina, uma combinaç
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Felldin, Markus. "Machine Learning Methods for Fault Classification." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-183132.

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This project, conducted at Ericsson AB, investigates the feasibility of implementing machine learning techniques in order to classify dump files for more effi cient trouble report routing. The project focuses on supervised machine learning methods and in particular Bayesian statistics. It shows that a program utilizing Bayesian methods can achieve well above random prediction accuracy. It is therefore concluded that machine learning methods may indeed become a viable alternative to human classification of trouble reports in the near future.<br>Detta examensarbete, utfört på Ericsson AB, ämnar
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Khan, Muhammad Naeem Ahmed. "Digital Forensics using Machine Learning Methods." Thesis, University of Sussex, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.487975.

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The increase in computer related crimes, with particular reference to internet crimes, has led to an increasing demand for state-of-the-art digital forensics. Reconstruction of the past events in chronological order is crucial for digital forensic investigations to pinpoint the execution of relevant application programs and the files manipulated by those applications. The event reconstruction process can be made more objective and rigorous by employing mathematical techniques due to their sound theoretical foundations. The focus of this research is to explore the effectiveness of employing mac
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Li, Limin, and 李丽敏. "Machine learning methods for computational biology." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2010. http://hub.hku.hk/bib/B44546749.

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Chlon, Leon. "Machine learning methods for cancer immunology." Thesis, University of Cambridge, 2017. https://www.repository.cam.ac.uk/handle/1810/268068.

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Tumours are highly heterogeneous collections of tissues characterised by a repertoire of heavily mutated and rapidly proliferating cells. Evading immune destruction is a fundamental hallmark of cancer, and elucidating the contextual basis of tumour-infiltrating leukocytes is pivotal for improving immunotherapy initiatives. However, progress in this domain is hindered by an incomplete characterisation of the regulatory mechanisms involved in cancer immunity. Addressing this challenge, this thesis is formulated around a fundamental line of inquiry: how do we quantitatively describe the immune sy
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Chang, Allison An. "Integer optimization methods for machine learning." Thesis, Massachusetts Institute of Technology, 2012. http://hdl.handle.net/1721.1/72643.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2012.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (p. 129-137).<br>In this thesis, we propose new mixed integer optimization (MIO) methods to ad- dress problems in machine learning. The first part develops methods for supervised bipartite ranking, which arises in prioritization tasks
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Lowe, Robert Alexander. "Investigating machine learning methods in chemistry." Thesis, University of Cambridge, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.610567.

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Berry, Jeffrey James. "Machine Learning Methods for Articulatory Data." Diss., The University of Arizona, 2012. http://hdl.handle.net/10150/223348.

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Humans make use of more than just the audio signal to perceive speech. Behavioral and neurological research has shown that a person's knowledge of how speech is produced influences what is perceived. With methods for collecting articulatory data becoming more ubiquitous, methods for extracting useful information are needed to make this data useful to speech scientists, and for speech technology applications. This dissertation presents feature extraction methods for ultrasound images of the tongue and for data collected with an Electro-Magnetic Articulograph (EMA). The usefulness of these featu
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Marakani, Sumeesha. "Employee Matching Using Machine Learning Methods." Thesis, Blekinge Tekniska Högskola, Institutionen för datavetenskap, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-18493.

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Background: Expertise retrieval is an information retrieval technique that focuses on techniques to identify the most suitable ’expert’ for a task from a list of individuals. Objectives: This master thesis is a collaboration with Volvo Cars to attempt applying this concept and match employees based on information that was extracted from an internal tool of the company. In this tool, the employees describe themselves in free-flowing text. This text is extracted from the tool and analyzed using Natural Language Processing (NLP) techniques. Methods: Through the course of this project, various tec
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Books on the topic "Methods of machine learning"

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Li, Hang. Machine Learning Methods. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-3917-6.

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Savoy, Jacques. Machine Learning Methods for Stylometry. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-53360-1.

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Steven, Minton, and Symposium on Learning Methods for Planning Systems (1991 : Stanford University), eds. Machine learning methods for planning. M. Kaufmann, 1993.

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G, Carbonell Jaime, ed. Machine learning: Paradigms and methods. MIT Press, 1990.

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Silhavy, Radek, and Petr Silhavy, eds. Machine Learning Methods in Systems. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-70595-3.

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Fielding, Alan H., ed. Machine Learning Methods for Ecological Applications. Springer US, 1999. http://dx.doi.org/10.1007/978-1-4615-5289-5.

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Hutter, Frank. Automated Machine Learning: Methods, Systems, Challenges. Springer Nature, 2019.

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Zhang, Cha. Ensemble Machine Learning: Methods and Applications. Springer US, 2012.

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Fielding, Alan H. Machine Learning Methods for Ecological Applications. Springer US, 1999.

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Vodrahalli, Kiran Nagesh. Resource-Efficient Methods in Machine Learning. [publisher not identified], 2022.

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Book chapters on the topic "Methods of machine learning"

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Geetha, T. V., and S. Sendhilkumar. "Performance Evaluation and Ensemble Methods." In Machine Learning. Chapman and Hall/CRC, 2023. http://dx.doi.org/10.1201/9781003290100-8.

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Chu, Liu. "Machine Learning Methods." In Uncertainty Quantification of Stochastic Defects in Materials. CRC Press, 2021. http://dx.doi.org/10.1201/9781003226628-7.

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Ghosh, Shyamasree, and Rathi Dasgupta. "Machine Learning Methods." In Machine Learning in Biological Sciences. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8881-2_3.

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Lista, Luca. "Machine Learning." In Statistical Methods for Data Analysis. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-19934-9_11.

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Lampropoulos, Aristomenis S., and George A. Tsihrintzis. "Cascade Recommendation Methods." In Machine Learning Paradigms. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19135-5_6.

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Cios, Krzysztof J., Witold Pedrycz, and Roman W. Swiniarski. "Machine Learning." In Data Mining Methods for Knowledge Discovery. Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-5589-6_6.

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Dawson, Catherine. "Machine learning." In A–Z of Digital Research Methods. Routledge, 2019. http://dx.doi.org/10.4324/9781351044677-30.

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Li, Hang. "Introduction to Machine Learning and Supervised Learning." In Machine Learning Methods. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-3917-6_1.

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Li, Hang. "Support Vector Machine." In Machine Learning Methods. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-3917-6_7.

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Mannor, Shie, Xin Jin, Jiawei Han, et al. "Kernel Methods." In Encyclopedia of Machine Learning. Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_430.

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Conference papers on the topic "Methods of machine learning"

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Elsersy, Wael Farouk, Moataz Samy, and Ahmed ElShamy. "XSS Attack Detection Using Machine Learning." In 2024 Intelligent Methods, Systems, and Applications (IMSA). IEEE, 2024. http://dx.doi.org/10.1109/imsa61967.2024.10652622.

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Elsersy, Wael Farouk, Ahmed ElShamy, and Moataz Samy. "Ransomware Detection Using Machine Learning Algorithms." In 2024 Intelligent Methods, Systems, and Applications (IMSA). IEEE, 2024. http://dx.doi.org/10.1109/imsa61967.2024.10652659.

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Zhang, Yihan, Bowen Deng, Amanda J. Wright, Michael G. Somekh, Michael P. Pounds, and Andrew J. Parkes. "Machine Learning Estimate the Optical Properties of Tissue." In Adaptive Optics: Methods, Analysis and Applications. Optica Publishing Group, 2024. http://dx.doi.org/10.1364/aopt.2024.oth4f.2.

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A method to extract scattering coefficients from random media is presented. A deep learning network is trained from Monte Carlo simulations. Using angular and spatial information together greatly improved robustness and accuracy over previous approaches. (tel: +44 7536964914, e-mail: eexyz67@nottingham.ac.uk).
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Badholia, Abhishek, Tarun Dhar Diwan, Preeti Narooka, Pravin B. Khatkale, Ankit Vishnoi, and Keshav Kaushik. "Sentiment Analysis Using Machine Learning Methods." In 2024 International Conference on Intelligent & Innovative Practices in Engineering & Management (IIPEM). IEEE, 2024. https://doi.org/10.1109/iipem62726.2024.10925794.

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Hickmann, M. Lautaro, Markus Lange, and Hans-Martin Rieser. "Enhancing Machine Learning with Quantum Methods." In ESANN 2025. Ciaco - i6doc.com, 2025. https://doi.org/10.14428/esann/2025.es2025-29.

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Atia, Hend Abdelbakey, Magdy Aboul-Ela, Christina Albert Reyad, and Nancy Awadallah Awad. "Online Payments Fraud Detection Using Machine Learning Techniques." In 2024 Intelligent Methods, Systems, and Applications (IMSA). IEEE, 2024. http://dx.doi.org/10.1109/imsa61967.2024.10652834.

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Singh, Amitojdeep, Sourya Sengupta, and Vasudevan Lakshminarayanan. "Glaucoma diagnosis using transfer learning methods." In Applications of Machine Learning, edited by Michael E. Zelinski, Tarek M. Taha, Jonathan Howe, Abdul A. Awwal, and Khan M. Iftekharuddin. SPIE, 2019. http://dx.doi.org/10.1117/12.2529429.

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Murdock, Vanessa. "Mixed Methods Machine Learning." In SIGMOD/PODS '23: International Conference on Management of Data. ACM, 2023. http://dx.doi.org/10.1145/3555041.3589337.

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Raff, Jan-Henning. "Machine Learning for Basic Visual Research in Graphic Design." In 8th International Visual Methods Conference. AIJR Publisher, 2024. http://dx.doi.org/10.21467/proceedings.168.26.

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This paper explores the intersection of machine learning and graphic design, aiming to enhance visual analysis methodologies through the integration of domain-specific knowledge. A critical examination of existing machine learning approaches for visual analysis reveals their limitations and the need to integrate more design specific knowledge. The paper proposes two approaches to analyze spatial aspects of graphic design. The application of the proposed methods demonstrates the potential of machine learning to reconstruct the intuition of graphic designers and to automate visual analysis tasks
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Tymoteusz, Miller. "MACHINE LEARNING - METHOD OR SOLUTION?" In SCIENTIFIC PRACTICE: MODERN AND CLASSICAL RESEARCH METHODS, chair Polina Kozlovska, Adrianna Łobodzińska, Klaudia Lewita, Julia Żejmo, and Oliwia Kaczanowska. European Scientific Platform, 2023. http://dx.doi.org/10.36074/logos-22.12.2023.058.

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Reports on the topic "Methods of machine learning"

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Vesselinov, Velimir Valentinov. TensorDecompostions : Unsupervised machine learning methods. Office of Scientific and Technical Information (OSTI), 2019. http://dx.doi.org/10.2172/1493534.

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Xu, Yuesheng. Adaptive Kernel Based Machine Learning Methods. Defense Technical Information Center, 2012. http://dx.doi.org/10.21236/ada588768.

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Moral, Rafael. Introduction to Machine Learning. Instats Inc., 2024. http://dx.doi.org/10.61700/qfxukp14jlpfd1478.

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This comprehensive workshop provides a thorough introduction to machine learning, focusing on both theoretical concepts and practical applications using R. Designed for PhD students, professors, and researchers, it covers essential techniques such as supervised and unsupervised learning, dimension reduction, and tree-based methods, enhancing participants' data analysis skills and research capabilities.
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Zhang, Tong. Multi-Stage Convex Relaxation Methods for Machine Learning. Defense Technical Information Center, 2013. http://dx.doi.org/10.21236/ada580533.

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Alonso-Robisco, Andrés, José Manuel Carbó, and José Manuel Carbó. Machine Learning methods in climate finance: a systematic review. Banco de España, 2023. http://dx.doi.org/10.53479/29594.

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Preventing the materialization of climate change is one of the main challenges of our time. The involvement of the financial sector is a fundamental pillar in this task, which has led to the emergence of a new field in the literature, climate finance. In turn, the use of Machine Learning (ML) as a tool to analyze climate finance is on the rise, due to the need to use big data to collect new climate-related information and model complex non-linear relationships. Considering the proliferation of articles in this field, and the potential for the use of ML, we propose a review of the academic lite
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Sankhye, Sidharth. Machine Learning Methods for Quality Prediction in Manufacturing Inspection. Iowa State University, 2020. http://dx.doi.org/10.31274/cc-20240624-975.

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Flaxman, Seth. Statistical Machine Learning for Researchers. Instats Inc., 2023. http://dx.doi.org/10.61700/3sz8pzpbpsg2i469.

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This workshop is designed to empower researchers with the fundamentals of machine learning using R. Participants will learn the key principles that make machine learning so effective, powering the modern AI and deep learning revolution. Through hands-on exercises, participants will gain experience applying a variety of flexible and scalable statistical machine learning methods to analyze datasets and build effective predictive models. An official Instats certificate of completion is provided along with 2 ECTS Equivalent points.
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Flaxman, Seth. Statistical Machine Learning for Researchers. Instats Inc., 2023. http://dx.doi.org/10.61700/wu1mihoap95h0469.

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This workshop is designed to empower researchers with the fundamentals of machine learning using R. Participants will learn the key principles that make machine learning so effective, powering the modern AI and deep learning revolution. Through hands-on exercises, participants will gain experience applying a variety of flexible and scalable statistical machine learning methods to analyze datasets and build effective predictive models. An official Instats certificate of completion is provided along with 2 ECTS Equivalent points.
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Badolato, Luca, Ari Gabriel Decter-Frain, Nicolas Irons, et al. Predicting individual-level longevity with statistical and machine learning methods. Max Planck Institute for Demographic Research, 2023. http://dx.doi.org/10.4054/mpidr-wp-2023-008.

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Jesneck, Jonathan, and Joseph Lo. Modular Machine Learning Methods for Computer-Aided Diagnosis of Breast Cancer. Defense Technical Information Center, 2004. http://dx.doi.org/10.21236/ada430017.

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