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Journal articles on the topic 'Theory, Machine learning'

Author: Grafiati

Published: 4 June 2021

Last updated: 8 February 2022

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1

CHASE, HUNTER, and JAMES FREITAG. "MODEL THEORY AND MACHINE LEARNING." Bulletin of Symbolic Logic 25, no. 03 (2019): 319–32. http://dx.doi.org/10.1017/bsl.2018.71.

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AbstractAbout 25 years ago, it came to light that a single combinatorial property determines both an important dividing line in model theory (NIP) and machine learning (PAC-learnability). The following years saw a fruitful exchange of ideas between PAC-learning and the model theory of NIP structures. In this article, we point out a new and similar connection between model theory and machine learning, this time developing a correspondence between stability and learnability in various settings of online learning. In particular, this gives many new examples of mathematically interesting classes w

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Petrova, O., and K. Bobriekhova. "DEVELOPING ADISTANCECOURSE «THEORY OF SYSTEMSIN MACHINE LEARNING PROBLEMS»." Transactions of Kremenchuk Mykhailo Ostrohradskyi National University 6 (December 27, 2019): 54–59. http://dx.doi.org/10.30929/1995-0519.2019.6.54-59.

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Huang, Guang-Bin, Qin-Yu Zhu, and Chee-Kheong Siew. "Extreme learning machine: Theory and applications." Neurocomputing 70, no. 1-3 (2006): 489–501. http://dx.doi.org/10.1016/j.neucom.2005.12.126.

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4

Tze-Leung Lai and S. Yakowitz. "Machine learning and nonparametric bandit theory." IEEE Transactions on Automatic Control 40, no. 7 (1995): 1199–209. http://dx.doi.org/10.1109/9.400491.

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Vanchurin, Vitaly. "Toward a theory of machine learning." Machine Learning: Science and Technology 2, no. 3 (2021): 035012. http://dx.doi.org/10.1088/2632-2153/abe6d7.

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6

Jackson, A. H. "Machine learning." Expert Systems 5, no. 2 (1988): 132–50. http://dx.doi.org/10.1111/j.1468-0394.1988.tb00341.x.

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7

Khare, Ashish, Moongu Jeon, Ishwar K. Sethi, and Benlian Xu. "Machine Learning Theory and Applications for Healthcare." Journal of Healthcare Engineering 2017 (2017): 1–2. http://dx.doi.org/10.1155/2017/5263570.

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8

Tanaka, Toshiyuki. "Mean-field theory of Boltzmann machine learning." Physical Review E 58, no. 2 (1998): 2302–10. http://dx.doi.org/10.1103/physreve.58.2302.

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9

E, Weinan. "Machine Learning: Mathematical Theory and Scientific Applications." Notices of the American Mathematical Society 66, no. 11 (2019): 1. http://dx.doi.org/10.1090/noti1994.

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Bianco, Michael J., Peter Gerstoft, James Traer, et al. "Machine learning in acoustics: Theory and applications." Journal of the Acoustical Society of America 146, no. 5 (2019): 3590–628. http://dx.doi.org/10.1121/1.5133944.

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Ding, Shifei, Han Zhao, Yanan Zhang, Xinzheng Xu, and Ru Nie. "Extreme learning machine: algorithm, theory and applications." Artificial Intelligence Review 44, no. 1 (2013): 103–15. http://dx.doi.org/10.1007/s10462-013-9405-z.

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12

Herrera-Ibatá, Diana M. "Machine Learning and Perturbation Theory Machine Learning (PTML) in Medicinal Chemistry, Biotechnology, and Nanotechnology." Current Topics in Medicinal Chemistry 21, no. 7 (2021): 649–60. http://dx.doi.org/10.2174/1568026621666210121153413.

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Recently, different authors have reported Perturbation Theory (PT) methods combined with machine learning (ML) to obtain PTML (PT + ML) models. They have applied PTML models to the study of different biological systems. Here we present one state-of-art review about the different applications of PTML models in Organic Synthesis, Medicinal Chemistry, Protein Research, and Technology. The aim of the models is to find relations between the molecular descriptors and the biological characteristics to predict key properties of new compounds. An area where the ML has been very useful is the drug disco

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13

Howard Miller, Alfred. "Using unsupervised machine learning to model tax practice learning theory." International Journal of Engineering & Technology 7, no. 2.4 (2018): 109. http://dx.doi.org/10.14419/ijet.v7i2.4.13019.

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The aim of this study was to utilize unsupervised machine learning framework to explore a dataset comprised of assessed output by Bachelors of Business, Taxation learners over four successive semesters. The researcher sought to motivate deployment of an evidence-supported, data-driven approach to understand the scope of student learning from a bachelor’s degree in business class taxation class, as a tool for accreditation reporting purposes. Outcomes from the data analysis identified four factors; two related to tax and two related to learning. These factors are, tax theory, and tax practice,

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Suppes, Patrick, and Michael Böttner. "Robotic machine learning of anaphora." Robotica 16, no. 4 (1998): 425–31. http://dx.doi.org/10.1017/s0263574798000022.

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Our contribution tackles the problem of learning to understand anaphoric references in the context of robotic machine learning; e.g. Get the large screw. Put it in the left hole. Our solution assumes the probabilistic theory of learning spelt out in earlier publications. Associations are formed probabilistically between constituents of the verbal command and constituents of a presupposed internal representation. The theory is extended, as a first step, to anaphora by learning how to distinguish between incorrect surface depth and the correct tree-structure depth of the anaphoric references.

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15

Lim, Daniel. "Philosophy through Machine Learning." Teaching Philosophy 43, no. 1 (2020): 29–46. http://dx.doi.org/10.5840/teachphil202018116.

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In a previous article (2019), I motivated and defended the idea of teaching philosophy through computer science. In this article, I will further develop this idea and discuss how machine learning can be used for pedagogical purposes because of its tight affinity with philosophical issues surrounding induction. To this end, I will discuss three areas of significant overlap: (i) good / bad data and David Hume’s so-called Problem of Induction, (ii) validation and accommodation vs. prediction in scientific theory selection and (iii) feature engineering and Nelson Goodman’s so-called New Riddle of

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16

Bleidorn, Wiebke, and Christopher James Hopwood. "Using Machine Learning to Advance Personality Assessment and Theory." Personality and Social Psychology Review 23, no. 2 (2018): 190–203. http://dx.doi.org/10.1177/1088868318772990.

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Machine learning has led to important advances in society. One of the most exciting applications of machine learning in psychological science has been the development of assessment tools that can powerfully predict human behavior and personality traits. Thus far, machine learning approaches to personality assessment have focused on the associations between social media and other digital records with established personality measures. The goal of this article is to expand the potential of machine learning approaches to personality assessment by embedding it in a more comprehensive construct vali

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17

Ai, Lun, Stephen H. Muggleton, Céline Hocquette, Mark Gromowski, and Ute Schmid. "Beneficial and harmful explanatory machine learning." Machine Learning 110, no. 4 (2021): 695–721. http://dx.doi.org/10.1007/s10994-020-05941-0.

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AbstractGiven the recent successes of Deep Learning in AI there has been increased interest in the role and need for explanations in machine learned theories. A distinct notion in this context is that of Michie’s definition of ultra-strong machine learning (USML). USML is demonstrated by a measurable increase in human performance of a task following provision to the human of a symbolic machine learned theory for task performance. A recent paper demonstrates the beneficial effect of a machine learned logic theory for a classification task, yet no existing work to our knowledge has examined the

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18

Molina, Mario, and Filiz Garip. "Machine Learning for Sociology." Annual Review of Sociology 45, no. 1 (2019): 27–45. http://dx.doi.org/10.1146/annurev-soc-073117-041106.

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Machine learning is a field at the intersection of statistics and computer science that uses algorithms to extract information and knowledge from data. Its applications increasingly find their way into economics, political science, and sociology. We offer a brief introduction to this vast toolbox and illustrate its current uses in the social sciences, including distilling measures from new data sources, such as text and images; characterizing population heterogeneity; improving causal inference; and offering predictions to aid policy decisions and theory development. We argue that, in addition

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19

Procaccia, Arieal D. "Towards a theory of incentives in machine learning." ACM SIGecom Exchanges 7, no. 2 (2008): 1–5. http://dx.doi.org/10.1145/1399589.1399595.

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20

Alzubi, Jafar, Anand Nayyar, and Akshi Kumar. "Machine Learning from Theory to Algorithms: An Overview." Journal of Physics: Conference Series 1142 (November 2018): 012012. http://dx.doi.org/10.1088/1742-6596/1142/1/012012.

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Foreman, Sam, Joel Giedt, Yannick Meurice, and Judah Unmuth-Yockey. "RG-inspired machine learning for lattice field theory." EPJ Web of Conferences 175 (2018): 11025. http://dx.doi.org/10.1051/epjconf/201817511025.

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Machine learning has been a fast growing field of research in several areas dealing with large datasets. We report recent attempts to use renormalization group (RG) ideas in the context of machine learning. We examine coarse graining procedures for perceptron models designed to identify the digits of the MNIST data. We discuss the correspondence between principal components analysis (PCA) and RG flows across the transition for worm configurations of the 2D Ising model. Preliminary results regarding the logarithmic divergence of the leading PCA eigenvalue were presented at the conference. More

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22

Yakowitz, S., and J. Mai. "Methods and theory for off-line machine learning." IEEE Transactions on Automatic Control 40, no. 1 (1995): 161–65. http://dx.doi.org/10.1109/9.362878.

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23

Coqueret, Guillaume. "Machine Learning in Finance: From Theory to Practice." Quantitative Finance 21, no. 1 (2020): 9–10. http://dx.doi.org/10.1080/14697688.2020.1828609.

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24

Chen, Ziheng, and Hongshik Ahn. "Item Response Theory Based Ensemble in Machine Learning." International Journal of Automation and Computing 17, no. 5 (2020): 621–36. http://dx.doi.org/10.1007/s11633-020-1239-y.

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25

Ertuğrul, Ömer Faruk, and Mehmet Emin Tağluk. "A novel machine learning method based on generalized behavioral learning theory." Neural Computing and Applications 28, no. 12 (2016): 3921–39. http://dx.doi.org/10.1007/s00521-016-2314-8.

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26

Jin, Chi, Praneeth Netrapalli, Rong Ge, Sham M. Kakade, and Michael I. Jordan. "On Nonconvex Optimization for Machine Learning." Journal of the ACM 68, no. 2 (2021): 1–29. http://dx.doi.org/10.1145/3418526.

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Gradient descent (GD) and stochastic gradient descent (SGD) are the workhorses of large-scale machine learning. While classical theory focused on analyzing the performance of these methods in convex optimization problems, the most notable successes in machine learning have involved nonconvex optimization, and a gap has arisen between theory and practice. Indeed, traditional analyses of GD and SGD show that both algorithms converge to stationary points efficiently. But these analyses do not take into account the possibility of converging to saddle points. More recent theory has shown that GD an

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Spagnolo, Nicolò, Alessandro Lumino, Emanuele Polino, Adil S. Rab, Nathan Wiebe, and Fabio Sciarrino. "Machine Learning for Quantum Metrology." Proceedings 12, no. 1 (2019): 28. http://dx.doi.org/10.3390/proceedings2019012028.

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Phase estimation represents a significant example to test the application of quantum theory for enhanced measurements of unknown physical parameters. Several recipes have been developed, allowing to define strategies to reach the ultimate bounds in the asymptotic limit of a large number of trials. However, in certain applications it is crucial to reach such bound when only a small number of probes is employed. Here, we discuss an asymptotically optimal, machine learning based, adaptive single-photon phase estimation protocol that allows us to reach the standard quantum limit when a very limite

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28

Rzeszótko, Jarosław, and Sinh Hoa Nguyen. "Machine Learning for Traffic Prediction." Fundamenta Informaticae 119, no. 3-4 (2012): 407–20. http://dx.doi.org/10.3233/fi-2012-745.

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29

Amari, Shun-ichi, and Noboru Murata. "Statistical Theory of Learning Curves under Entropic Loss Criterion." Neural Computation 5, no. 1 (1993): 140–53. http://dx.doi.org/10.1162/neco.1993.5.1.140.

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The present paper elucidates a universal property of learning curves, which shows how the generalization error, training error, and the complexity of the underlying stochastic machine are related and how the behavior of a stochastic machine is improved as the number of training examples increases. The error is measured by the entropic loss. It is proved that the generalization error converges to H0, the entropy of the conditional distribution of the true machine, as H0 + m*/(2t), while the training error converges as H0 - m*/(2t), where t is the number of examples and m* shows the complexity o

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30

An, Chang. "Student Status Supervision in Ideological and Political Machine Teaching Based on Machine Learning." E3S Web of Conferences 275 (2021): 03028. http://dx.doi.org/10.1051/e3sconf/202127503028.

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Under the premise of active in the field of machine learning, this paper takes online teaching system of ideological and Political education as an example to study machine learning and machine teaching system. In order to specifically understand the current situation of the construction and application of machine teaching based on supervised teaching of ideological and political theory courses in local colleges and universities, this experiment first conducted a statistical analysis of the learning results of the surveyed classes in two semesters from March 2020 to December 2020. The experimen

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31

Amali, Said, Nour-eddine EL Faddouli, and Ali Boutoulout. "Machine Learning and Graph Theory to Optimize Drinking Water." Procedia Computer Science 127 (2018): 310–19. http://dx.doi.org/10.1016/j.procs.2018.01.127.

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32

Elacio, Alexen A. "Machine Learning Integration of Herzberg’s Theory using C4.5 Algorithm." International Journal of Advanced Trends in Computer Science and Engineering 9, no. 1.1 S I (2020): 57–63. http://dx.doi.org/10.30534/ijatcse/2020/1191.12020.

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33

Blay, Vincent, Toshiyuki Yokoi, and Humbert González-Díaz. "Perturbation Theory–Machine Learning Study of Zeolite Materials Desilication." Journal of Chemical Information and Modeling 58, no. 12 (2018): 2414–19. http://dx.doi.org/10.1021/acs.jcim.8b00383.

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34

López Kleine, Liliana. "Principles and Theory for Data Mining and Machine Learning." Journal of the Royal Statistical Society: Series A (Statistics in Society) 173, no. 3 (2010): 691–92. http://dx.doi.org/10.1111/j.1467-985x.2010.00646_3.x.

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35

Veronese, Elisa, Umberto Castellani, Denis Peruzzo, Marcella Bellani, and Paolo Brambilla. "Machine Learning Approaches: From Theory to Application in Schizophrenia." Computational and Mathematical Methods in Medicine 2013 (2013): 1–12. http://dx.doi.org/10.1155/2013/867924.

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In recent years, machine learning approaches have been successfully applied for analysis of neuroimaging data, to help in the context of disease diagnosis. We provide, in this paper, an overview of recent support vector machine-based methods developed and applied in psychiatric neuroimaging for the investigation of schizophrenia. In particular, we focus on the algorithms implemented by our group, which have been applied to classify subjects affected by schizophrenia and healthy controls, comparing them in terms of accuracy results with other recently published studies. First we give a descript

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36

Zobeiry, Navid, Johannes Reiner, and Reza Vaziri. "Theory-guided machine learning for damage characterization of composites." Composite Structures 246 (August 2020): 112407. http://dx.doi.org/10.1016/j.compstruct.2020.112407.

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37

Brehmer, Johann, Kyle Cranmer, Irina Espejo, et al. "Constraining effective field theories with machine learning." EPJ Web of Conferences 245 (2020): 06026. http://dx.doi.org/10.1051/epjconf/202024506026.

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An important part of the Large Hadron Collider (LHC) legacy will be precise limits on indirect effects of new physics, framed for instance in terms of an effective field theory. These measurements often involve many theory parameters and observables, which makes them challenging for traditional analysis methods. We discuss the underlying problem of “likelihood-free” inference and present powerful new analysis techniques that combine physics insights, statistical methods, and the power of machine learning. We have developed MadMiner, a new Python package that makes it straightforward to apply t

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38

Hopkins, Richard. "David Kolb's Experiential Learning Machine." Journal of Phenomenological Psychology 24, no. 1 (1993): 46–62. http://dx.doi.org/10.1163/156916293x00035.

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AbstractThis article is a review of David Kolb's program of work on learning styles and experiential learning, which I find to be a problematic instance of psychologism. I argue that Kolb's approach ignores the process nature of experience and that attractive as it may be instrumentally, it ultimately breaks down under the weight of its structuralist reductions. Kolb attempts to account for experiential learning without a coherent theory of experience, such as might have been found in phenomenology, which he virtually ignores. Thus, Kolb neglects the constitutive effects of the noetic-noemic c

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39

Moeslund, Thomas B., Sergio Escalera, Gholamreza Anbarjafari, Kamal Nasrollahi, and Jun Wan. "Statistical Machine Learning for Human Behaviour Analysis." Entropy 22, no. 5 (2020): 530. http://dx.doi.org/10.3390/e22050530.

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40

Golden, Richard M. "Adaptive Learning Algorithm Convergence in Passive and Reactive Environments." Neural Computation 30, no. 10 (2018): 2805–32. http://dx.doi.org/10.1162/neco_a_01117.

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Although the number of artificial neural network and machine learning architectures is growing at an exponential pace, more attention needs to be paid to theoretical guarantees of asymptotic convergence for novel, nonlinear, high-dimensional adaptive learning algorithms. When properly understood, such guarantees can guide the algorithm development and evaluation process and provide theoretical validation for a particular algorithm design. For many decades, the machine learning community has widely recognized the importance of stochastic approximation theory as a powerful tool for identifying e

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41

Shi, Lei, Xin Ming Ma, and Xiao Hong Hu. "Combination with Machine Learning Algorithms for the Classification in E-Bussiness." Advanced Materials Research 230-232 (May 2011): 625–28. http://dx.doi.org/10.4028/www.scientific.net/amr.230-232.625.

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E-bussiness has grown rapidly in the last decade and massive amount of data on customer purchases, browsing pattern and preferences has been generated. Classification of electronic data plays a pivotal role to mine the valuable information and thus has become one of the most important applications of E-bussiness. Support Vector Machines are popular and powerful machine learning techniques, and they offer state-of-the-art performance. Rough set theory is a formal mathematical tool to deal with incomplete or imprecise information and one of its important applications is feature selection. In thi

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42

Mohr, Felix, Marcel Wever, Alexander Tornede, and Eyke Hullermeier. "Predicting Machine Learning Pipeline Runtimes in the Context of Automated Machine Learning." IEEE Transactions on Pattern Analysis and Machine Intelligence 43, no. 9 (2021): 3055–66. http://dx.doi.org/10.1109/tpami.2021.3056950.

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43

Deist, Timo M., Andrew Patti, Zhaoqi Wang, David Krane, Taylor Sorenson, and David Craft. "Simulation-assisted machine learning." Bioinformatics 35, no. 20 (2019): 4072–80. http://dx.doi.org/10.1093/bioinformatics/btz199.

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Abstract Motivation In a predictive modeling setting, if sufficient details of the system behavior are known, one can build and use a simulation for making predictions. When sufficient system details are not known, one typically turns to machine learning, which builds a black-box model of the system using a large dataset of input sample features and outputs. We consider a setting which is between these two extremes: some details of the system mechanics are known but not enough for creating simulations that can be used to make high quality predictions. In this context we propose using approxima

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44

Bezerra, Arthur Coelho, and Marco Antônio de Almeida. "Rage against the machine learning." Brazilian Journal of Information Science 14, no. 2 Abr-Jun (2020): 06–23. http://dx.doi.org/10.36311/1981-1640.2020.v14n2.02.p6.

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Before being an exaltation to Luddites (the English workers from the 19th century who actually destroyed textile machinery as a form of protest) or to some sort of technophobic movement, the provocative pun contained in the title of this article carries a methodological proposal, in the field of critical theory of information, to build a diagnosis about the algorithmic filtering of information, which reveals itself to be a structural characteristic of the new regime of information that brings challenges to human emancipation. Our analysis starts from the concept of mediation to problematize th

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Brumen, Boštjan, Aleš Černezel, and Leon Bošnjak. "Overview of Machine Learning Process Modelling." Entropy 23, no. 9 (2021): 1123. http://dx.doi.org/10.3390/e23091123.

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Much research has been conducted in the area of machine learning algorithms; however, the question of a general description of an artificial learner’s (empirical) performance has mainly remained unanswered. A general, restrictions-free theory on its performance has not been developed yet. In this study, we investigate which function most appropriately describes learning curves produced by several machine learning algorithms, and how well these curves can predict the future performance of an algorithm. Decision trees, neural networks, Naïve Bayes, and Support Vector Machines were applied to 130

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46

Vinterbo, S. A. "Privacy: a machine learning view." IEEE Transactions on Knowledge and Data Engineering 16, no. 8 (2004): 939–48. http://dx.doi.org/10.1109/tkde.2004.31.

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Et. al., Zakoldaev D. A. ,. "Machine Learning Methods Performance Evaluation*." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 2 (2021): 2664–66. http://dx.doi.org/10.17762/turcomat.v12i2.2284.

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In this paper, we describe an approach for air pollution modeling in the data incompleteness scenarios, when the sensors cover the monitoring area only partially. The fundamental calculus and metrics of using machine learning modeling algorithms are presented. Moreover, the assessing indicators and metrics for machine learning methods performance evaluation are described. Based on the conducted analysis, conclusions on the most appropriate evaluation approaches are made.

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Et. al., Mathew Chacko,. "Cyber-Physical Quality Systems in Manufacturing." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 2 (2021): 2006–18. http://dx.doi.org/10.17762/turcomat.v12i2.1805.

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Digital Twin-based Cyber-Physical Quality System (DT-CPQS) concept involves automated quality checking, simulation, and prediction of manufacturing operations to improve production efficiency and flexibility as part of Industrie4.0 initiatives. DT-CPQS will provide the basis for the manufacturing process to march towards an autonomous quality platform for zero defect manufacturing in the future. Analysing sensor data from the CNC machine and vision monitoring system it was concluded that there was enough signal data to detect quality issues in a part being machined in advance using statistical

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Ran, Zhi-Yong, and Bao-Gang Hu. "Parameter Identifiability in Statistical Machine Learning: A Review." Neural Computation 29, no. 5 (2017): 1151–203. http://dx.doi.org/10.1162/neco_a_00947.

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This review examines the relevance of parameter identifiability for statistical models used in machine learning. In addition to defining main concepts, we address several issues of identifiability closely related to machine learning, showing the advantages and disadvantages of state-of-the-art research and demonstrating recent progress. First, we review criteria for determining the parameter structure of models from the literature. This has three related issues: parameter identifiability, parameter redundancy, and reparameterization. Second, we review the deep influence of identifiability on v

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Rezek, I., D. S. Leslie, S. Reece, et al. "On Similarities between Inference in Game Theory and Machine Learning." Journal of Artificial Intelligence Research 33 (October 23, 2008): 259–83. http://dx.doi.org/10.1613/jair.2523.

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In this paper, we elucidate the equivalence between inference in game theory and machine learning. Our aim in so doing is to establish an equivalent vocabulary between the two domains so as to facilitate developments at the intersection of both ﬁelds, and as proof of the usefulness of this approach, we use recent developments in each ﬁeld to make useful improvements to the other. More speciﬁcally, we consider the analogies between smooth best responses in ﬁctitious play and Bayesian inference methods. Initially, we use these insights to develop and demonstrate an improved algorithm for learnin

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