Relevant bibliographies by topics / Applied learning

Contents

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

Academic literature on the topic 'Applied learning'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Applied learning"

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Rudrappa, Gujanatti. "Machine Learning Models Applied for Rainfall Prediction." Revista Gestão Inovação e Tecnologias 11, no. 3 (2021): 179–87. http://dx.doi.org/10.47059/revistageintec.v11i3.1926.

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M, Ms Jeevitha, Abinaya K, Sharmila R, Arthi S, and Karthika B. "DeepLeuko-(Deep learning applied to leukemia detection)." International Journal of Research Publication and Reviews 6, no. 4 (2025): 3463–70. https://doi.org/10.55248/gengpi.6.0425.1426.

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Israel, Steven A., Philip Sallee, Franklin Tanner, Jonathan Goldstein, and Shane Zabel. "Applied Machine Learning Strategies." IEEE Potentials 39, no. 3 (2020): 38–42. http://dx.doi.org/10.1109/mpot.2019.2927899.

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Yusfiani, Marnida. "Teacher performance on students learning outcomes in applied chemistry." Jurnal Pendidikan Kimia 12, no. 1 (2020): 20–25. http://dx.doi.org/10.24114/jpkim.v12i1.17709.

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Pehrson, Lea, Carsten Lauridsen, and Michael Nielsen. "Machine learning and deep learning applied in ultrasound." Ultraschall in der Medizin - European Journal of Ultrasound 39, no. 04 (2018): 379–81. http://dx.doi.org/10.1055/a-0642-9545.

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Dr Dara Hamid Mohammed. "APPLIED LINGUISTICS AND LANGUAGE LEARNING." Researchers World : Journal of Arts, Science and Commerce VIII, no. 3(1) (2017): 123–28. http://dx.doi.org/10.18843/rwjasc/v8i3(1)/18.

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Saravanan, T. "ICT Applied Cooperative Learning Environment." International Journal for Research in Applied Science and Engineering Technology V, no. VIII (2017): 1630–35. http://dx.doi.org/10.22214/ijraset.2017.8231.

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Fabián A. Aldana Herrera et al.,, Fabián A. Aldana Herrera et al ,. "Machine Learning Applied to Networking." International Journal of Mechanical and Production Engineering Research and Development 10, no. 6 (2020): 347–52. http://dx.doi.org/10.24247/ijmperddec202039.

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Anees, Amir, Iqtadar Hussain, Umar M. Khokhar, Fawad Ahmed, and Sajjad Shaukat. "Machine Learning and Applied Cryptography." Security and Communication Networks 2022 (January 27, 2022): 1–3. http://dx.doi.org/10.1155/2022/9797604.

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Lipovetsky, Stan, and Jong-Min Kim. "Machine Learning in Applied Statistics." Model Assisted Statistics and Applications 12, no. 3 (2017): 193–94. http://dx.doi.org/10.3233/mas-170404.

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Dissertations / Theses on the topic "Applied 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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Galimberti, Jaqueson Kingeski. "Adaptive learning for applied macroeconomics." Thesis, University of Manchester, 2013. https://www.research.manchester.ac.uk/portal/en/theses/adaptive-learning-for-applied-macroeconomics(cde517d7-d552-4a53-a442-c584262c3a8f).html.

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The literature on bounded rationality and learning in macroeconomics has often used recursive algorithms to depict the evolution of agents' beliefs over time. In this thesis we assess this practice from an applied perspective, focusing on the use of such algorithms for the computation of forecasts of macroeconomic variables. Our analysis develops around three issues we find to have been previously neglected in the literature: (i) the initialization of the learning algorithms; (ii) the determination and calibration of the learning gains, which are key parameters of the algorithms' specification
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Bargagli, Stoffi Falco Johannes. "Essays on applied machine learning." Thesis, IMT Alti Studi Lucca, 2020. http://e-theses.imtlucca.it/311/1/BargagliStoffi_phdthesis.pdf.

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In this Dissertation, we deal with a series of applications of machine learning in the fields of social and health sciences. We introduce a set of novelties in the traditional usage of machine learning algorithms for predictive and causal inference tasks. In Part 1, we explore the field of machine learning for causal inference and we introduce two innovative techniques that combine state-of-the-art machine learning algorithms with causal inference methodologies. In the first Chapter, we introduce a novel Bayesian tree-based methodology to draw causal inference on heterogeneous effects
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Serafini, Sara. "Machine Learning applied to OCR tasks." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019.

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The content of this thesis describes the work done during a six-month internship at Datalogic, in its research laboratories in Pasadena (CA). The aim of my research was to implement and evaluate a classifier as part of an industrial OCR system for learning purposes and to see how well it could work in comparison to current best Datalogic products, since it might be simpler/faster, it might be a good alternative for implementing on an embedded system (where current Datalogic products may not be able to run fast enough).
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Mohd, Nawi Abdullah. "Applied Drama in English Language Learning." Thesis, University of Canterbury. School of Literacies and Arts in Education, 2014. http://hdl.handle.net/10092/9584.

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This thesis is a reflective exploration of the use and impact of using drama pedagogies in the English as a Second Language (ESL)/ English as a Foreign Language (EFL) classroom. It stems from the problem of secondary school English language learning in Malaysia, where current teaching practices appear to have led to the decline of the standard of English as a second language in school leavers and university graduates (Abdul Rahman, 1997; Carol Ong Teck Lan, Anne Leong Chooi Khaun, & Singh, 2011; Hazita et al., 2010; Nalliah & Thiyagarajah, 1999). This problem resonates with my own experiences
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Drake, Adam C. "Practical Improvements in Applied Spectral Learning." BYU ScholarsArchive, 2010. https://scholarsarchive.byu.edu/etd/2546.

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Spectral learning algorithms, which learn an unknown function by learning a spectral representation of the function, have been widely used in computational learning theory to prove many interesting learnability results. These algorithms have also been successfully used in real-world applications. However, previous work has left open many questions about how to best use these methods in real-world learning scenarios. This dissertation presents several significant advances in real-world spectral learning. It presents new algorithms for finding large spectral coefficients (a key sub-problem in sp
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Domeniconi, Federico. "Deep Learning Techniques applied to Photometric Stereo." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/20031/.

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La tesi si focalizza sullo studio dello stato dell’arte della fotometria stereo con deep learning: Self-calibrating Deep Photometric Stereo Networks. Il modello è composto è composto di due reti, la prima predice la direzione e l’intensità delle luci, la seconda predice le normali della superficie. L’obiettivo della tesi è individuare i limiti del modello e capire se possa essere modifcato per avere buone prestazioni anche in scenari reali. Il progetto di tesi è basato su fine-tuning, una tecnica supervisionata di transfer learning. Per questo scopo un nuovo dataset è stato creato acquisendo i
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Wang, Shihai. "Boosting learning applied to facial expression recognition." Thesis, University of Manchester, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.511940.

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Weintraub, Ben Julian. "Learning control applied to a model helicopter." Thesis, Massachusetts Institute of Technology, 1994. http://hdl.handle.net/1721.1/49921.

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Idowu, Samuel O. "Applied Machine Learning in District Heating System." Licentiate thesis, Luleå tekniska universitet, Datavetenskap, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-68486.

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In an increasingly applied domain of pervasive computing, sensing devices are being deployed progressively for data acquisition from various systems through the use of technologies such as wireless sensor networks. Data obtained from such systems are used analytically to advance or improve system performance or efficiency. The possibility to acquire an enormous amount of data from any target system has made machine learning a useful approach for several large-scale analytical solutions. Machine learning has proved viable in the area of the energy sector, where the global demand for energy and
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Books on the topic "Applied learning"

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Fergus, Paul, and Carl Chalmers. Applied Deep Learning. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-04420-5.

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Michelucci, Umberto. Applied Deep Learning. Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-3790-8.

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Forsyth, David. Applied Machine Learning. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-18114-7.

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Schonlau, Matthias. Applied Statistical Learning. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-33390-3.

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Michelucci, Umberto. Advanced Applied Deep Learning. Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-4976-5.

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Sternberg, Robert J. Applied intelligence. Cambridge University Press, 2008.

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Seedhouse, Paul, Steve Walsh, and Chris Jenks, eds. Conceptualising 'Learning' in Applied Linguistics. Palgrave Macmillan UK, 2010. http://dx.doi.org/10.1057/9780230289772.

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Beysolow II, Taweh. Applied Reinforcement Learning with Python. Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-5127-0.

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Jean-Paul, Doignon, and Doignon Jean-Paul, eds. Learning spaces: Interdisciplinary applied mathematics. Springer, 2011.

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Ganguly, Santanu. Quantum Machine Learning: An Applied Approach. Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-7098-1.

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Book chapters on the topic "Applied learning"

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Schinko-Fischli, Susanne. "Applied improvisation." In Learning Social Skills Virtually. Routledge, 2021. http://dx.doi.org/10.4324/9781003172765-1.

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Yi-Man, Au, and John O'Toole. "Learning applied theatre." In Social Capacity Building through Applied Theatre. Routledge, 2024. http://dx.doi.org/10.4324/9781003426387-7.

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Rasmussen, Erin B., Casey J. Clay, W. David Pierce, and Carl D. Cheney. "Applied Behavior Analysis." In Behavior Analysis and Learning, 7th ed. Routledge, 2022. http://dx.doi.org/10.4324/9781003202622-13.

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Pierce, W. David, and Carl D. Cheney. "Applied Behavior Analysis." In Behavior Analysis and Learning. Routledge, 2017. http://dx.doi.org/10.4324/9781315200682-13.

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Singh, Nirbhay N., and Ivan L. Beale. "Learning Disabilities." In Applied Clinical Psychology. Springer US, 1990. http://dx.doi.org/10.1007/978-1-4613-0983-3_21.

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Savin-Baden, Maggi, and David Burden. "Applied Models for Learning." In The Metaverse for Learning and Education. Chapman and Hall/CRC, 2024. http://dx.doi.org/10.1201/9781003413875-4.

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Dhanaraj, Anne, Yao Teck Ong, and Tit Meng Lim. "Stem Applied Learning Program." In Amplifying Informal Science Learning. Routledge, 2023. http://dx.doi.org/10.4324/9781003145387-22.

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Forsyth, David. "Learning to Classify." In Applied Machine Learning. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-18114-7_1.

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Schonlau, Matthias. "Statistical Learning: Concepts." In Applied Statistical Learning. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-33390-3_2.

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Shinde, Aditya, and Swati Shinde. "Insights into AI, Machine Learning, and Deep Learning." In Applied Artificial Intelligence. CRC Press, 2023. http://dx.doi.org/10.1201/9781003324430-4.

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Conference papers on the topic "Applied learning"

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Barmada, Bashar, Abha Chitalia, and Eltahir Kabbar. "INDUSTRY INTERNSHIPS: BENEFITS, CHALLENGES AND APPLIED MODELS." In 17th International Conference on Education and New Learning Technologies. IATED, 2025. https://doi.org/10.21125/edulearn.2025.1058.

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Bransby, Martin. "Machine Learning Applied to Signals of Opportunity." In IAF Space Communications and Navigation Symposium, Held at the 75th International Astronautical Congress (IAC 2024). International Astronautical Federation (IAF), 2024. https://doi.org/10.52202/078363-0021.

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Ballester, Manuel, Christoph Würsch, Emilio Marquez, Florian Willomitzer, and A. K. Katsaggelos. "Optical Characterization of Thin Films from Transmission Data using Deep Learning." In Applied Industrial Spectroscopy. Optica Publishing Group, 2024. https://doi.org/10.1364/ais.2024.fd1.1.

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Chalyan, Tatevik, Indy Magnus, and Heidi Ottevaere. "Spectroscopy Combined with Machine Learning to Study Oak Barrels Reusability in Wine Industry." In Applied Industrial Spectroscopy. Optica Publishing Group, 2024. https://doi.org/10.1364/ais.2024.atu1g.2.

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To monitor the reusability of oak barrels in the wine industry for highly efficient wine aging purposes, various non-invasive spectroscopic techniques are studied in combination with machine learning algorithms.
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Chandnani, Kanchan, Deepti Chavan, Pratiti Desai, and Dhananjay R. Kalbande. "Machine learning applied to human learning." In 2013 Annual IEEE India Conference (INDICON). IEEE, 2013. http://dx.doi.org/10.1109/indcon.2013.6725908.

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Cao, Yifan, Qian Bai, and Yifan Huang. "APPLIED INTERNET TEACHING INNOVATION." In 14th International Conference on Education and New Learning Technologies. IATED, 2022. http://dx.doi.org/10.21125/edulearn.2022.0611.

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Paulus, Sachar, Thomas Smits, Tobias Becht, and Serife Kol. "Ubiquitous Learning Applied to Coding." In ECSEE'18: European Conference of Software Engineering Education 2018. ACM, 2018. http://dx.doi.org/10.1145/3209087.3209104.

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Olcan, Dragan, and Darko Ninkovic. "Machine Learning in Applied Electromagnetics." In International Conference IcETRAN. ETRAN Society, Academic Mind, Belgrade, 2024. https://doi.org/10.69994/11ic24004.

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We outline the ongoing research efforts in machine learning applied to electromagnetics at the School of Electrical Engineering, University of Belgrade, Serbia. Namely, in the past two years, our group has published multiple results regarding machine learning for antenna design and optimization, as well as applications for analyzing the emanated electromagnetic field from flat-panel monitors. We focus on finding the optimal topology of multilayer perceptron (neural) networks for the considered electromagnetic systems and assess the accuracy and efficiency of such models. The first reported exa
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Abelairas-Etxebarria, Patricia, and Jon Mentxaka. "SOCIAL NETWORKS APPLIED TO UNIVERSITY." In International Conference on Education and New Learning Technologies. IATED, 2017. http://dx.doi.org/10.21125/edulearn.2017.0275.

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Unda, Xavier L., and Valentina Ramos. "EXPECTANCY THEORY APPLIED TO AN EDUCATIONAL CONTEXT: A LONGITUDINAL STUDY APPLIED IN POSTGRADUATE COURSES." In International Conference on Education and New Learning Technologies. IATED, 2016. http://dx.doi.org/10.21125/edulearn.2016.2027.

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Reports on the topic "Applied learning"

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Bannister, Joseph, Wei-Min Shen, Joseph Touch, Feili Hou, and Venkata Pingali. Applied Learning Networks (ALN). Defense Technical Information Center, 2007. http://dx.doi.org/10.21236/ada462328.

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Curnow, Christina, Rebecca Mulvaney, Robert Calderon, et al. Advanced Learning Theories Applied to Leadership Development. Defense Technical Information Center, 2006. http://dx.doi.org/10.21236/ada462784.

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Brown, David. Machine learning applied to classifying neutron resonances. Office of Scientific and Technical Information (OSTI), 2020. http://dx.doi.org/10.2172/1673302.

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Hobbs, Noah. Machine learning applied to the Los Alamos aquifer. Office of Scientific and Technical Information (OSTI), 2023. http://dx.doi.org/10.2172/1992217.

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Griffiths, M., H. A. J. Russell, and C E Logan. Machine learning applied to geoscience: Geo-referenced character recognition. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2020. http://dx.doi.org/10.4095/321092.

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Gorshkova, G. N. Distance learning course «Accounting for the direction of Applied Informatics». OFERNIO, 2021. http://dx.doi.org/10.12731/ofernio.2021.24860.

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Sun, Hongbin, Pradeep Ramuhalli, and Ryan Meyer. An Assessment of Machine Learning Applied to Ultrasonic Nondestructive Evaluation. Office of Scientific and Technical Information (OSTI), 2024. http://dx.doi.org/10.2172/2320378.

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Kleinosova, N. P. Distance learning course "Modern applied business packages", training direction 38.03.05"Business Informatics. OFERNIO, 2018. http://dx.doi.org/10.12731/ofernio.2018.23683.

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de Luis, Mercedes, Emilio Rodríguez, and Diego Torres. Machine learning applied to active fixed-income portfolio management: a Lasso logit approach. Banco de España, 2023. http://dx.doi.org/10.53479/33560.

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The use of quantitative methods constitutes a standard component of the institutional investors’ portfolio management toolkit. In the last decade, several empirical studies have employed probabilistic or classification models to predict stock market excess returns, model bond ratings and default probabilities, as well as to forecast yield curves. To the authors’ knowledge, little research exists into their application to active fixed-income management. This paper contributes to filling this gap by comparing a machine learning algorithm, the Lasso logit regression, with a passive (buy-and-hold)
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Canto, Patricia, ed. Learning Modes, Types of Innovation and Economic Performance. Universidad de Deusto, 2012. http://dx.doi.org/10.18543/xebj1954.

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This contribution focuses on a current heated debate on learning modes employed by the firms, and their impact on innovation and economic output. The interactive approach developed by the Scandinavian school on innovation systems characterized two key learning modes as „science and technology-based innovation? (STI) and „learning-by-doing, by-using and by-interacting-based innovation (DUI). This work analyzes first the separate and combined impact of such modes of learning and innovation on two types of innovation output: product and process. In this operation, this work produces interesting a
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