Relevant bibliographies by topics / Learning theory

Contents

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

Academic literature on the topic 'Learning theory'

Author: Grafiati
Published: 4 June 2021
Last updated: 8 June 2024

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Journal articles on the topic "Learning theory"

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Chandler, David, and Hokyu Hwang. "Learning From Learning Theory." Journal of Management 41, no. 5 (February 27, 2015): 1446–76. http://dx.doi.org/10.1177/0149206315572698.

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Zhou, Ding-Xuan, Qiang Wu, and Yiming Ying. "Learning Theory." Abstract and Applied Analysis 2014 (2014): 1–2. http://dx.doi.org/10.1155/2014/138960.

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Illeris, Knud. "Workplace learning and learning theory." Journal of Workplace Learning 15, no. 4 (July 2003): 167–78. http://dx.doi.org/10.1108/13665620310474615.

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HERSHBERGER, WAYNE A. "Control Theory and Learning Theory." American Behavioral Scientist 34, no. 1 (September 1990): 55–66. http://dx.doi.org/10.1177/0002764290034001006.

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Lee, Hye-Soo. "Spinozist-Deleuzian Learning and the Narrative of Apprenticeship: Pride and Prejudice." Criticism and Theory Society of Korea 28, no. 1 (February 28, 2023): 213–39. http://dx.doi.org/10.19116/theory.2023.28.1.213.

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I examine how Gilles Deleuze’s discussion of “learning” is predicated on Spinoza’s “common notion,” and how Jane Austen’s Pride and Prejudice, a representative classical Bildungsroman in Britain, also works as a narrative of “Spinozist-Deleuzian learning.” Deviating from Lukacs’s definition of Bildungsroman as the maturation of “a problematic individual” and his/her “reconciliation” with the society, narratives of Spinozist-Deleuzian learning remind us of the importance of learning (apprenticeship) as an indispensable part of our lives even in the 21st century. Spinoza’s Ethics presents learning or apprenticeship as a crucial facet of his ethical project of active liberty where common notion as “a strange harmony of reason and imagination” plays a decisive role. Deleuze’s account of “learning” reformulates the differences between Spinozist common notion and Cartesian concept of truth (i.e. correspondence of an object with the mind’s representation of it) into the distinction of “learning” and “knowledge.” Simply put, while learning is a problem or a problematic field, knowledge is a solution; they are as distant as possible in nature. Pride and Prejudice exemplifies a process of learning in Spinozist-Deleuzian sense where Elizabeth and Mr. Darcy encounter to realize passive affects like pride as the core part of their selves and proceed to joy and affirmation through the formation of common notions. Furthermore, Austen’s novel evidences that the novel reader’s forrmation of common notions with characters and also with the narrator through sympathy and distancing is the mechanism of novel reading more pivotal than identification or sympathy. The novel is a singular space where imagination as the necessary condition of human knowledge is unfolded as well as what Spinoza calls the virtue (eminence) of the mind, i.e. the mind’s meta-power of being aware that it imagines as it imagines.
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KROHN, MARVIN D. "Social Learning Theory:." Theoretical Criminology 3, no. 4 (November 1999): 462–76. http://dx.doi.org/10.1177/1362480699003004006.

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Page, Denys. "Theory of learning." Education + Training 19, no. 5 (December 31, 1993): 132. http://dx.doi.org/10.1108/eb016513.

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Kearns, Michael J., and Umesh V. Vazirani. "Computational Learning Theory." ACM SIGACT News 26, no. 1 (March 1995): 43–45. http://dx.doi.org/10.1145/203610.606411.

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Wu, Yuhai. "Statistical Learning Theory." Technometrics 41, no. 4 (November 1999): 377–78. http://dx.doi.org/10.1080/00401706.1999.10485951.

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Hanna, Richard C., Victoria L. Crittenden, and William F. Crittenden. "Social Learning Theory." Journal of Marketing Education 35, no. 1 (January 31, 2013): 18–25. http://dx.doi.org/10.1177/0273475312474279.

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Dissertations / Theses on the topic "Learning theory"

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Olteanu, Alin. "A Peircean theory of learning." Thesis, University of Roehampton, 2015. https://pure.roehampton.ac.uk/portal/en/studentthesis/a-peircean-theory-of-learning(a3afed52-8626-4918-b41a-ca350502d46d).html.

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I develop a theory of learning grounded in Charles Peirce’s semiotics. This endeavour comes in the context of the iconic (phenomenological) turn in semiotics, which resulted in a Peircean renaissance, and of the growing semiotic trend in education. Peirce’s semiotics offers insights into the phenomenon of learning and contains an implicit philosophy of education. The application of Peirce’s phenomenological categories to education reveals the semiosic character of education. Learning, education, and research constitute a triad, having the structure of a sign (phenomenon of signification). As such, they are correspondingly governed by Peirce’s three criteria of evolution: chance, necessity, and love. Therefore, Peirce’s theory of education can only be understood in the context of his theory of evolution. I develop three central arguments: (1) that according to Peirce’s taxonomy of signs, learning is the evolution of signification from the Icon sign type to the Argument sign type, (2) that learning is the Universe’s way of discovering itself through life forms, thus being both an evolutionary factor and an explanation for the emergence of life and (3) that learning can only be fulfilled in self-denying love for the other. Using Peirce’s taxonomy of signs I analyse the student/teacher relation, explaining how the passage from Icon to Argument proceeds and how learning is fulfilled in self-denying love.
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Liang, Annie. "Economic Theory and Statistical Learning." Thesis, Harvard University, 2016. http://nrs.harvard.edu/urn-3:HUL.InstRepos:33493561.

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This dissertation presents three independent essays in microeconomic theory. Chapter 1 suggests an alternative to the common prior assumption, in which agents form beliefs by learning from data, possibly interpreting the data in different ways. In the limit as agents observe increasing quantities of data, the model returns strict solutions of a limiting complete information game, but predictions may diverge substantially for small quantities of data. Chapter 2 (with Jon Kleinberg and Sendhil Mullainathan) proposes use of machine learning algorithms to construct benchmarks for “achievable" predictive accuracy. The paper illustrates this approach for the problem of predicting human-generated random sequences. We find that leading models explain approximately 10-15% of predictable variation in the problem. Chapter 3 considers the problem of how to interpret inconsistent choice data, when the observed departures from the standard model (perfect maximization of a single preference) may emerge either from context-dependencies in preference or from stochastic choice error. I show that if preferences are “simple" in the sense that they consist only of a small number of context-dependencies, then the analyst can use a proposed optimization problem to recover the true number of underlying context-dependent preferences.
Economics
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Carlucci, Lorenzo. "Some cognitively-motivated learning paradigms in Algorithmic Learning Theory." Access to citation, abstract and download form provided by ProQuest Information and Learning Company; downloadable PDF file 0.68 Mb., p, 2006. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&res_dat=xri:pqdiss&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&rft_dat=xri:pqdiss:3220797.

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Rossiter, P. G. "Organisational improvement through learning organisation theory." Thesis, University of Salford, 2007. http://usir.salford.ac.uk/2256/.

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A research study was conducted of the management theories and quality philosophies that have been expounded throughout the twentieth century. This study included the modem thinking for quality improvement and business excellence to include the modem concept of Learning Organisations. This research project was undertaken with the aim of producing a framework based on the culture of Learning Organisation Theory and including within it the external influences on such a culture. The framework consisted on a core of human values, divided into five areas that are deemed important to learning organisations. These were surrounded by the basic values of Trust, Honesty and Openness thus protecting the core from outside influence. Elements from traditional management systems theory provided the outer casing for the framework, these elements influencing the core for both good and bad. The contents of the framework were then studied in three organisations of differing background with a view to firmly establishing the elements and areas within the framework for validity in these three organisations. The common theme between all the organisations chosen was that they had all in the recent past been involved in major management and internal change. One study involved the development of a questionnaire and supporting matrices in order to identify the areas and elements of the framework, thus establishing their existence. Active research techniques were used in the other studies in order to establish both 'why' the elements are important and any interrelationship between the areas. As a result of these studies suggestions for modification to the framework were established in order to strengthen the thinking and these were encompassed in to the framework. Probably the most significant of these changes was the inclusion of 'Leadership' as being a major factor in the filtering of undesirable elements. The outcome from the research is that the aim was achieved and a framework was developed that, for the first time, was drawn up in such a way that the elements and areas can easily be recognised and an understanding of what they represent is clearly shown. The reasons as to why these elements are important are also established. This is regarded as an advancement in this field of study.
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Gilli, Mario. "Equilibrium and learning in game theory." Thesis, University of Cambridge, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.389813.

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Coleman, Donnie Steve. "Technological Immersion Learning: A Grounded Theory." Diss., Virginia Tech, 2017. http://hdl.handle.net/10919/75155.

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The Technological Immersion Learning Theory (TILT) was developed through a classic grounded theory study in the seminal tradition of Glaser and Strauss (1967) and Glaser (1978, 1992, 1998, 2001, 2007). The purpose of the study was to investigate an exemplary case of self-determined technology enthusiasts in the hopes of generating a substantive grounded theory that conceptualizes their experiences and concerns. Twelve unstructured interviews of amateur radio enthusiasts from the eastern United States provided the initial / primary data for this study. Experimenting and self-teaching in technological activities was highlighted as the main concern of the participants. The basic social process (BSP) of technological immersion learning (TIL) emerged as a theoretical construct and core variable that illuminates the experiences of individuals immersed in a community of practice, where hands-on engagement with technology is a primary activity. Adventuring, Affirmation, Doing Technology, Experimenting, Overcoming Challenge, Self-teaching, and Social Networking were properties of technological immersion learning that interact dialectically in an amplifying causal loop, with Problem solving and Designing as active sub processes in response to unmet challenges. TIL occurs cyclically in three stages, beginning with Induction, a credentialing stage wherein the neophyte is prepared with the necessary knowledge and skill to become a novice participant in an activity. The transition from Induction into the Immersion phase is a status passage whereby the novice is absorbed into the technical culture of the group and commences autonomous active participation in hands-on experimenting. Hands-on experiences with experimenting, problem solving and social interactions provide diverse learning and affirmation for the doer and multiple sources of feedback that promote sustained engagement. The transition into the Maturation phase proceeds gradually over time, with prolonged engagement and cumulative gains in knowledge, skill, and experience. Maturation is a quasi-stable state that remains responsive to new contexts as a random-walk process, wherein trigger events can initiate new cycles of technological immersion learning in a perpetually evolving process of personal development. Engagement, Empowerment, and Self-Actualization are underlying dimensions of the TIL basic social process that provide the impetus for continued persistence and personal development.
Ph. D.
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Chakeri, Alireza. "Scalable Unsupervised Learning with Game Theory." Scholar Commons, 2017. http://scholarcommons.usf.edu/etd/6616.

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Recently dominant sets, a generalization of the notion of the maximal clique to edge-weighted graphs, have proven to be an effective tool for unsupervised learning and have found applications in different domains. Although, they were initially established using optimization and graph theory concepts, recent work has shown fascinating connections with evolutionary game theory, that leads to the clustering game framework. However, considering size of today's data sets, existing methods need to be modified in order to handle massive data. Hence, in this research work, first we address the limitations of the clustering game framework for large data sets theoretically. We propose a new important question for the clustering community ``How can a cluster of a subset of a dataset be a cluster of the entire dataset?''. We show that, this problem is a coNP-hard problem in a clustering game framework. Thus, we modify the definition of a cluster from a stable concept to a non-stable but optimal one (Nash equilibrium). By experiments we show that this relaxation does not change the qualities of the clusters practically. Following this alteration and the fact that equilibriums are generally compact subsets of vertices, we design an effective strategy to find equilibriums representing well distributed clusters. After finding such equilibriums, a linear game theoretic relation is proposed to assign vertices to the clusters and partition the graph. However, the method inherits a space complexity issue, that is the similarities between every pair of objects are required which proves practically intractable for large data sets. To overcome this limitation, after establishing necessary theoretical tools for a special type of graphs that we call vertex-repeated graphs, we propose the scalable clustering game framework. This approach divides a data set into disjoint tractable size chunks. Then, the exact clusters of the entire data are approximated by the clusters of the chunks. In fact, the exact equilibriums of the entire graph is approximated by the equilibriums of the subsets of the graph. We show theorems that enable significantly improved time complexity for the model. The applications include, but are not limited to, the maximum weight clique problem, large data clustering and image segmentation. Experiments have been done on random graphs and the DIMACS benchmark for the maximum weight clique problem and on magnetic resonance images (MRI) of the human brain consisting of about 4 million examples for large data clustering. Also, on the Berkeley Segmentation Dataset, the proposed method achieves results comparable to the state of the art, providing a parallel framework for image segmentation and without any training phase. The results show the effectiveness and efficiency of our approach. In another part of this research work, we generalize the clustering game method to cluster uncertain data where the similarities between the data points are not exactly known, that leads to the uncertain clustering game framework. Here, contrary to the ensemble clustering approaches, where the results of different similarity matrices are combined, we focus on the average utilities of an uncertain game. We show that the game theoretical solutions provide stable clusters even in the presence of severe uncertainties. In addition, based on this framework, we propose a novel concept in uncertain data clustering so that every subset of objects can have a ''cluster degree''. Extensive experiments on real world data sets, as well as on the Berkeley image segmentation dataset, confirm the performance of the proposed method. And finally, instead of dividing a graph into chunks to make the clustering scalable, we study the effect of the spectral sparsification method based on sampling by effective resistance on the clustering outputs. Through experimental and theoretical observations, we show that the clustering results obtained from sparsified graphs are very similar to the results of the original non-sparsified graphs. The rand index is always at about 0.9 to 0.99 in our experiments even when lots of sparsification is done.
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Neykov, Matey. "Three Aspects of Biostatistical Learning Theory." Thesis, Harvard University, 2015. http://nrs.harvard.edu/urn-3:HUL.InstRepos:17467395.

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In the present dissertation we consider three classical problems in biostatistics and statistical learning - classification, variable selection and statistical inference. Chapter 2 is dedicated to multi-class classification. We characterize a class of loss functions which we deem relaxed Fisher consistent, whose local minimizers not only recover the Bayes rule but also the exact conditional class probabilities. Our class encompasses previously studied classes of loss-functions, and includes non-convex functions, which are known to be less susceptible to outliers. We propose a generic greedy functional gradient-descent minimization algorithm for boosting weak learners, which works with any loss function in our class. We show that the boosting algorithm achieves geometric rate of convergence in the case of a convex loss. In addition we provide numerical studies and a real data example which serve to illustrate that the algorithm performs well in practice. In Chapter 3, we provide insights on the behavior of sliced inverse regression in a high-dimensional setting under a single index model. We analyze two algorithms: a thresholding based algorithm known as diagonal thresholding and an L1 penalization algorithm - semidefinite programming, and show that they achieve optimal (up to a constant) sample size in terms of support recovery in the case of standard Gaussian predictors. In addition, we look into the performance of the linear regression LASSO in single index models with correlated Gaussian designs. We show that under certain restrictions on the covariance and signal, the linear regression LASSO can also enjoy optimal sample size in terms of support recovery. Our analysis extends existing results on LASSO's variable selection capabilities for linear models. Chapter 4 develops general inferential framework for testing and constructing confidence intervals for high-dimensional estimating equations. Such framework has a variety of applications and allows us to provide tests and confidence regions for parameters estimated by algorithms such as the Dantzig Selector, CLIME and LDP among others, non of which has been previously equipped with inferential procedures.
Biostatistics
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Flynn, Michael. "Linguistics and General Process Learning Theory." University of Arizona Linguistics Circle, 1987. http://hdl.handle.net/10150/226547.

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This paper is sort of an extended footnote, with a faint Borgesian flavor. What I'm going to do is show how one rather prominent argument in the linguistics literature against one aspect of the research program of behaviorism fails to go through. But I'll also observe that this argument appears to have had no practical effect on linguistic investigations, and that many people seem to assume (tacitly, at least) that this argument fails anyway. So my remarks here don't move the field forward any, but what I hope they do is help to get us all a bit clearer about where we are. The argument I'll be examining, given by Noam Chomsky in Reflections on Language (Chomsky 1975), is against a point of view called "general process learning theory ", a view that regards one goal of psychological theorizing to be the discovery of laws of learning that hold across species and across domains of acquisition. Psychological theorizing is by no means a new development on the linguistics scene. It is true, I think, that in most cases the people who have thought about language (including but not limited to people we would call linguists) have done so against the backdrop of a psychological theory that they assumed to be at least on the right track, and the idea was often to see what you could make of language by applying the analytical tools that the given psychological theory made available. Bloomfield (1926) is an example of this. (For some discussion of Bloomfield's views on psychology, see Lyons 1978, chapter 3.) One also in this context thinks of Piaget, Skinner of course, as well as philosophers of the 17th and 18th centuries of both the continental Cartesian variety and the so-called British Empiricists. I also think it's true that Chomsky's impact on psychology is somewhat unusual in that the flow of influence is in the other direction; that is, the question is, "If human language is like this, then what must the mind be like ?" rather than the other way around. Be that as it may, Chomsky has been, by far and away, the leading expositor of the implications of linguistics for the study of the structure of the human mind. It goes without saying that the ramifications of this work have been very rich, the pivotal role of linguistics in the "cognitive sciences" being just one indication of its influence. One of the earliest engagements at discipline boundaries was Chomsky's forceful assault on B.F. Skinner's attempt to extend the domain of behaviorist psychology to human languages. It's this argument that I want to have another look at. To do this it will be useful to try to isolate several facets of the discussion. I should perhaps reiterate, for the connoisseurs of counterrevolution who I know are out there, that my conclusion will be a modest one. I will not be concluding that after all Skinner was right and Chomsky was wrong. On the contrary, I'm going to assume that this game is over, and has been for quite some time. My goal is to call attention to what I think is an Unsolved problem which acquires its interest because it bears on how we regard linguistics as influencing our judgment about the structure of the human mind.
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Kaiser, Alexander. "An enhanced theory of learning including learning from the future." IEEE computer society press, 2016. http://epub.wu.ac.at/4812/1/hicss%2Dpaper%2Dalexander.pdf.

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Previous studies showed that combining learning based on experiences in the past with learning from an envisioned future scenario results in more innovative and radical ideas as well as in a higher number of covered content domains. However, currently there is no holistic learning theory which integrates both sources of learning. The main purpose of our paper is to propose an enhanced theory of learning, linking the two most important sources of learning: learning from past experiences and learning from the future. Our suggested theory, which is based on the learning theory by Gregory Bateson, will be described in detail. Moreover, we will present some empirical experiences with the enhanced theory of learning. (author's abstract)
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Books on the topic "Learning theory"

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Shaydenko, Nadezhda, and Svetlana Kipurova. Learning theory. ru: INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1077726.

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The textbook is devoted to one of the sections of pedagogy - the theory of learning. The leading issues are the content of education; forms, methods, means of teaching; forms of organization of training. The main attention is paid to the educational system of Russia and modern trends in the development of education. The study of the theory of learning is important not only for the future teacher to master the system of knowledge, but also for the formation of students' didactic competencies. To do this, in addition to theoretical material, the textbook contains a significant amount of practical tasks, material for consolidating what has been studied, test questions and tasks. Meets the requirements of the federal state educational standards of higher education of the latest generation. For undergraduate students studying in psychological and pedagogical specialties, as well as for undergraduates of pedagogical specialties who do not have a basic pedagogical education.
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Bshouty, Nader H., and Claudio Gentile, eds. Learning Theory. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-72927-3.

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Lugosi, Gábor, and Hans Ulrich Simon, eds. Learning Theory. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11776420.

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Shawe-Taylor, John, and Yoram Singer, eds. Learning Theory. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/b98522.

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Auer, Peter, and Ron Meir, eds. Learning Theory. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/b137542.

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Ecological learning theory. London: Routledge, 1989.

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Watanabe, Osamu, and Takashi Yokomori, eds. Algorithmic Learning Theory. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-46769-6.

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Bshouty, Nader H., Gilles Stoltz, Nicolas Vayatis, and Thomas Zeugmann, eds. Algorithmic Learning Theory. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34106-9.

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Jantke, Klaus P., Takeshi Shinohara, and Thomas Zeugmann, eds. Algorithmic Learning Theory. Berlin, Heidelberg: Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/3-540-60454-5.

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Jain, Sanjay, Rémi Munos, Frank Stephan, and Thomas Zeugmann, eds. Algorithmic Learning Theory. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40935-6.

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Book chapters on the topic "Learning theory"

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Settles, Burr. "Theory." In Active Learning, 55–62. Cham: Springer International Publishing, 2012. http://dx.doi.org/10.1007/978-3-031-01560-1_6.

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Boddez, Yannick, Frank Baeyens, Dirk Hermans, and Tom Beckers. "Learning Theory." In The Wiley Handbook of Anxiety Disorders, 83–103. Chichester, UK: John Wiley & Sons, Ltd, 2014. http://dx.doi.org/10.1002/9781118775349.ch7.

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Vidyasagar, Mathukumalli. "Learning Theory." In Encyclopedia of Systems and Control, 628–31. London: Springer London, 2015. http://dx.doi.org/10.1007/978-1-4471-5058-9_227.

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Vidyasagar, Mathukumalli. "Learning Theory." In Encyclopedia of Systems and Control, 1–5. London: Springer London, 2014. http://dx.doi.org/10.1007/978-1-4471-5102-9_227-1.

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Gharehbaghi, Arash. "Learning Theory." In Deep Learning in Time Series Analysis, 13–35. Boca Raton: CRC Press, 2023. http://dx.doi.org/10.1201/9780429321252-3.

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Yang, Fan, and Zhenghong Dong. "Educational Theory." In Learning Path Construction in e-Learning, 15–29. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-1944-9_2.

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Bleakley, Alan, John Bligh, and Julie Browne. "Learning from Learning Theory." In Medical Education for the Future, 33–42. Dordrecht: Springer Netherlands, 2011. http://dx.doi.org/10.1007/978-90-481-9692-0_3.

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Case, John, and Samuel E. Moelius. "U-Shaped, Iterative, and Iterative-with-Counter Learning." In Learning Theory, 172–86. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-72927-3_14.

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Even-Dar, Eyal, Michael Kearns, Yishay Mansour, and Jennifer Wortman. "Regret to the Best vs. Regret to the Average." In Learning Theory, 233–47. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-72927-3_18.

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Lugosi, Gábor, Shie Mannor, and Gilles Stoltz. "Strategies for Prediction Under Imperfect Monitoring." In Learning Theory, 248–62. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-72927-3_19.

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Conference papers on the topic "Learning theory"

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Angluin, Dana. "Computational learning theory." In the twenty-fourth annual ACM symposium. New York, New York, USA: ACM Press, 1992. http://dx.doi.org/10.1145/129712.129746.

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Raghavan, Raghu. "Computational learning theory." In Aerospace Sensing, edited by Dennis W. Ruck. SPIE, 1992. http://dx.doi.org/10.1117/12.140091.

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Klement, Milan, and Jiří Dostál. "THEORY OF LEARNING AND E-LEARNING." In International Technology, Education and Development Conference. IATED, 2016. http://dx.doi.org/10.21125/inted.2016.0175.

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Simonsen, Henrik Køhler. "ECOSYSTEM LEARNING: AN INTEGRATED LEARNING THEORY?" In 18th International Technology, Education and Development Conference. IATED, 2024. http://dx.doi.org/10.21125/inted.2024.0551.

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BURLACU, Natalia. "Capstone Project: From Theory to Practice." In International Conference on Virtual Learning - VIRTUAL LEARNING - VIRTUAL REALITY (17th edition). The National Institute for Research & Development in Informatics - ICI Bucharest (ICI Publishing House), 2022. http://dx.doi.org/10.58503/icvl-v17y202205.

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"Learning Theory and Modeling." In 2006 16th IEEE Signal Processing Society Workshop on Machine Learning for Signal Processing. IEEE, 2006. http://dx.doi.org/10.1109/mlsp.2006.275538.

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Tian, Jing, Ming-hu Ha, Jun-hua Li, and Da-zeng Tian. "The Fuzzy- Number Based Key Theorem of Statistical Learning Theory." In 2006 International Conference on Machine Learning and Cybernetics. IEEE, 2006. http://dx.doi.org/10.1109/icmlc.2006.258536.

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Sun, Xiao-Jing, Chao Wang, Ming-Hu Ha, and Da-Zeng Tian. "The key theorem of learning theory based on hybrid variable." In 2011 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2011. http://dx.doi.org/10.1109/icmlc.2011.6016929.

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Tingyan Bi and Wenfang Yang. "Modern learning theory and u-learning studies." In 2011 International Conference on e-Education, Entertainment and e-Management (ICEEE). IEEE, 2011. http://dx.doi.org/10.1109/iceeem.2011.6137795.

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Yun-Chao Bai and Ming-Hu Ha. "The key theorem of statistical learning theory on possibility spaces." In Proceedings of 2005 International Conference on Machine Learning and Cybernetics. IEEE, 2005. http://dx.doi.org/10.1109/icmlc.2005.1527708.

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Reports on the topic "Learning theory"

1

Moody, John. Statistical Learning Theory and Algorithms. Fort Belvoir, VA: Defense Technical Information Center, February 1993. http://dx.doi.org/10.21236/ada270209.

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Glaser, Robert, and Miriam Bassok. Learning Theory and the Study of Instruction. Fort Belvoir, VA: Defense Technical Information Center, February 1989. http://dx.doi.org/10.21236/ada204744.

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Caponnetto, Andrea, and Yuan Yao. Adaptation for Regularization Operators in Learning Theory. Fort Belvoir, VA: Defense Technical Information Center, September 2006. http://dx.doi.org/10.21236/ada456686.

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Van Lehn, Kurt, Michelene T. Chi, William Baggett, and R. C. Murray. Towards a Theory of Learning During Tutoring. Fort Belvoir, VA: Defense Technical Information Center, November 1995. http://dx.doi.org/10.21236/ada301445.

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Caponnetto, Andrea. Optimal Rates for Regularization Operators in Learning Theory. Fort Belvoir, VA: Defense Technical Information Center, September 2006. http://dx.doi.org/10.21236/ada456685.

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Porter, Bruce W., Ray Bareiss, and Robert C. Holte. Concept Learning and Heuristic Classification in Weak-Theory Domains. Fort Belvoir, VA: Defense Technical Information Center, March 1990. http://dx.doi.org/10.21236/ada248064.

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Hanna, Rema, Sendhil Mullainathan, and Joshua Schwartzstein. Learning Through Noticing: Theory and Experimental Evidence in Farming. Cambridge, MA: National Bureau of Economic Research, September 2012. http://dx.doi.org/10.3386/w18401.

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Weichert, Robert S. Leadership Theory Taught in Air Force Distant Learning Programs. Fort Belvoir, VA: Defense Technical Information Center, March 2013. http://dx.doi.org/10.21236/ada590284.

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Goldman, Jeffery A. Machine Learning: A Comparative Study of Pattern Theory and C4.5. Fort Belvoir, VA: Defense Technical Information Center, June 1994. http://dx.doi.org/10.21236/ada285582.

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Sablina, V. A. The distance learning course «Theory of Computational Processes and Structures». OFERNIO, February 2018. http://dx.doi.org/10.12731/ofernio.2018.23537.

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