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Bibliografias temáticas / The learning space

Literatura científica selecionada sobre o tema "The learning space"

Autor: Grafiati

Publicado: 23 de junho de 2021

Última modificação: 5 de fevereiro de 2022

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Artigos de revistas
Teses / dissertações
Livros
Capítulos de livros
Trabalhos de conferências
Relatórios de organizações

Artigos de revistas sobre o assunto "The learning space":

1

Talbert, Robert, e Anat Mor-Avi. "A space for learning: An analysis of research on active learning spaces". Heliyon 5, n.º 12 (dezembro de 2019): e02967. http://dx.doi.org/10.1016/j.heliyon.2019.e02967.

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Fabisch, Alexander, Yohannes Kassahun, Hendrik Wöhrle e Frank Kirchner. "Learning in compressed space". Neural Networks 42 (junho de 2013): 83–93. http://dx.doi.org/10.1016/j.neunet.2013.01.020.

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3

Sedlmeier, Andreas, e Sebastian Feld. "Learning indoor space perception". Journal of Location Based Services 12, n.º 3-4 (2 de outubro de 2018): 179–214. http://dx.doi.org/10.1080/17489725.2018.1539255.

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4

Martland, Rebecca. "Space to lead learning". Early Years Educator 21, n.º 11 (2 de março de 2020): 18–20. http://dx.doi.org/10.12968/eyed.2020.21.11.18.

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Williamson, Andy, e Carolyn Nodder. "Extending the learning space". ACM SIGCAS Computers and Society 32, n.º 3 (30 de setembro de 2002): 1. http://dx.doi.org/10.1145/644618.644620.

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Prade, Henri, e Mathieu Serrurier. "Bipolar version space learning". International Journal of Intelligent Systems 23, n.º 10 (outubro de 2008): 1135–52. http://dx.doi.org/10.1002/int.20310.

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Hughes, Billie, Barry Kort e Jim Walters. "Virtual space learning MariMUSE". ACM SIGCUE Outlook 22, n.º 2 (abril de 1994): 17–22. http://dx.doi.org/10.1145/182815.182817.

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Wible, B. "Learning to Share Space". Science 331, n.º 6020 (24 de fevereiro de 2011): 988. http://dx.doi.org/10.1126/science.331.6020.988-a.

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Guan, Renchu, Xu Wang, Maurizio Marchese, Mary Qu Yang, Yanchun Liang e Chen Yang. "Feature space learning model". Journal of Ambient Intelligence and Humanized Computing 10, n.º 5 (9 de maio de 2018): 2029–40. http://dx.doi.org/10.1007/s12652-018-0805-4.

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Hillstrom-Svercek, Sandra. "Space: A learning center". Day Care & Early Education 12, n.º 4 (junho de 1985): 31–36. http://dx.doi.org/10.1007/bf01619854.

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Teses / dissertações sobre o assunto "The learning space":

1

Ameur, Foued ben Fredj. "Space-bounded learning algorithms /". Paderborn : Heinz Nixdorf Inst, 1996. http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&doc_number=007171235&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA.

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Kiddle, Rebecca. "Learning outside the box : designing social learning space". Thesis, Oxford Brookes University, 2011. https://radar.brookes.ac.uk/radar/items/f7b36f17-cf4f-4590-8dd7-e6df3ecfc1d2/1/.

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Social learning theory asserts that learning involves constructing knowledge through dialogue with others. Traditional learning spaces designed with didactic pedagogies in mind are often not flexible enough to cater to the range of social learning activities promoted by educationalists in classrooms today. This thesis investigates the interaction between social constructivist theories of learning and urban design, developing a body of social learning space design theory as well as space design principles which foster social learning in a university setting. The research uses an 'Enquiry by Design' methodology to develop the principles, basing this enquiry on two case studies: (i) a pilot study analysing an existing social learning space; the Simon Williams Undergraduate Centre, and (ii) an ongoing masterplanning project at Oxford Brookes University's Gipsy Lane campus, Space to Think.

3

Ferreira, Paulo Victor Rodrigues. "SRML: Space Radio Machine Learning". Digital WPI, 2017. https://digitalcommons.wpi.edu/etd-dissertations/199.

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Space-based communications systems to be employed by future artificial satellites, or spacecraft during exploration missions, can potentially benefit from software-defined radio adaptation capabilities. Multiple communication requirements could potentially compete for radio resources, whose availability of which may vary during the spacecraft's operational life span. Electronic components are prone to failure, and new instructions will eventually be received through software updates. Consequently, these changes may require a whole new set of near-optimal combination of parameters to be derived on-the-fly without instantaneous human interaction or even without a human in-the-loop. Thus, achieving a sufficiently set of radio parameters can be challenging, especially when the communication channels change dynamically due to orbital dynamics as well as atmospheric and space weather-related impairments. This dissertation presents an analysis and discussion regarding novel algorithms proposed in order to enable a cognition control layer for adaptive communication systems operating in space using an architecture that merges machine learning techniques employing wireless communication principles. The proposed cognitive engine proof-of-concept reasons over time through an efficient accumulated learning process. An implementation of the conceptual design is expected to be delivered to the SDR system located on the International Space Station as part of an experimental program. To support the proposed cognitive engine algorithm development, more realistic satellite-based communications channels are proposed along with rain attenuation synthesizers for LEO orbits, channel state detection algorithms, and multipath coefficients function of the reflector's electrical characteristics. The achieved performance of the proposed solutions are compared with the state-of-the-art, and novel performance benchmarks are provided for future research to reference.

4

Chardonnet, Lucile. "A Shared Learning Space inMidsommarkransen". Thesis, KTH, Arkitektur, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-223240.

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New school buildings are met with questions in growing cities like Stockholm: How to place them? Which scale? What use, degree of openness, and flexibility, and for whom? This reflection has been made in relation to a suburban area, resulting in a proposal for smaller schools supported by another, shared, building that welcomes more specific activities and is open to the public. It would offer more specialized and adapted spaces to cooking, sewing, music and dance classes, as well as a bigger library and would intensify their use.

5

Kumar, Shailesh. "Modular learning through output space decomposition /". Full text (PDF) from UMI/Dissertation Abstracts International, 2000. http://wwwlib.umi.com/cr/utexas/fullcit?p3004308.

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Qian, Jing. "Unsupervised learning in high-dimensional space". Thesis, Boston University, 2014. https://hdl.handle.net/2144/12951.

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Thesis (Ph.D.)--Boston University
In machine learning, the problem of unsupervised learning is that of trying to explain key features and find hidden structures in unlabeled data. In this thesis we focus on three unsupervised learning scenarios: graph based clustering with imbalanced data, point-wise anomaly detection and anomalous cluster detection on graphs. In the first part we study spectral clustering, a popular graph based clustering technique. We investigate the reason why spectral clustering performs badly on imbalanced and proximal data. We then propose the partition constrained minimum cut (PCut) framework based on a novel parametric graph construction method, that is shown to adapt to different degrees of imbalanced data. We analyze the limit cut behavior of our approach, and demonstrate the significant performance improvement through clustering and semi-supervised learning experiments on imbalanced data. [TRUNCATED]

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Nichols, B. "Reinforcement learning in continuous state- and action-space". Thesis, University of Westminster, 2014. https://westminsterresearch.westminster.ac.uk/item/967w8/reinforcement-learning-in-continuous-state-and-action-space.

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Reinforcement learning in the continuous state-space poses the problem of the inability to store the values of all state-action pairs in a lookup table, due to both storage limitations and the inability to visit all states sufficiently often to learn the correct values. This can be overcome with the use of function approximation techniques with generalisation capability, such as artificial neural networks, to store the value function. When this is applied we can select the optimal action by comparing the values of each possible action; however, when the action-space is continuous this is not possible. In this thesis we investigate methods to select the optimal action when artificial neural networks are used to approximate the value function, through the application of numerical optimization techniques. Although it has been stated in the literature that gradient-ascent methods can be applied to the action selection [47], it is also stated that solving this problem would be infeasible, and therefore, is claimed that it is necessary to utilise a second artificial neural network to approximate the policy function [21, 55]. The major contributions of this thesis include the investigation of the applicability of action selection by numerical optimization methods, including gradient-ascent along with other derivative-based and derivative-free numerical optimization methods,and the proposal of two novel algorithms which are based on the application of two alternative action selection methods: NM-SARSA [40] and NelderMead-SARSA. We empirically compare the proposed methods to state-of-the-art methods from the literature on three continuous state- and action-space control benchmark problems from the literature: minimum-time full swing-up of the Acrobot; Cart-Pole balancing problem; and a double pole variant. We also present novel results from the application of the existing direct policy search method genetic programming to the Acrobot benchmark problem [12, 14].

8

Saeed, Sabina, e Sabina Saeed. "Learning To Learn: A Look Into the Collaborative Learning Space". Thesis, The University of Arizona, 2017. http://hdl.handle.net/10150/625142.

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The Collaborative Learning Space is a recent addition to the University of Arizona that helps students engage in active learning. Prior to the incorporation of the CLS, courses were primarily taught in a lecture based format. After receiving a grant, the University of Arizona decided to use the money to develop new types of leaning spaces and classrooms to increase student engagement and active learning. These classrooms are not only effective for the students, but also they make a huge impact on the faculty. For my honors senior thesis, I worked with Dr. Cohen to get a closer look at how we learn and what learning is like in the CLS. We investigated what types of learning and teaching styles are used in the CLS, and how students and professors view this new space in comparison to a regular lecture based classroom. We also explored the literature on how active learning impacts students learning. Overall the teacher and student satisfaction with the new learning spaces was seen to be very high, and active learning was found to show improvements in different areas including information retention, critical thinking, study habits, student attitude, and problem-solving skills.

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Mackevicius, Emily Lambert. "Building a state space for song learning". Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/120871.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Brain and Cognitive Sciences, 2018.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 159-177).
Song learning circuitry is thought to operate using a unique representation of each moment within each song syllable. Distinct timestamps for each moment in the song have been observed in the premotor cortical nucleus HVC, where neurons burst in sparse sequences. However, such sparse sequences are not present in very young birds, which sing highly variable syllables of random lengths. Furthermore, young birds learn by imitating a tutor song, and it was previously unclear precisely how the experience of hearing a tutor might shape auditory, motor, and evaluation pathways in the songbird brain. My thesis presents a framework for how these pathways may assemble during early learning, using simple neural mechanisms. I start with a neural network model for how premotor sequences may grow and split. This model predicts that the sequence-generating nucleus HVC would receive rhythmically patterned training inputs. I found such a signal when I recorded neurons that project to HVC. When juvenile birds sing, these neurons burst at the beginning of each syllable, and when the birds listen to a tutor, neurons burst at the rhythm of the tutor's song. Bursts marking the beginning of every tutor syllable could seed chains of sequential activity in HVC that could be used to generate the bird's own song imitation. I next used functional calcium imaging to characterize HVC sequences before and after tutor exposure. Analysis of these datasets led us to develop a new method for unsupervised detection of neural sequences. Using this method, I was able to observe neural sequences even prior to tutor exposure. Some of these sequences could be tracked as new syllables emerged after tutor exposure, and some sequences appeared to become coupled to the new syllables. In light of my new data, I expand on previous models of song learning to form a detailed hypothesis for how simple neural processes may perform song learning from start to finish.
by Emily Lambert Mackevicius.
Ph. D.

10

Bellocchi, Alberto. "Learning in the third space : a sociocultural perspective on learning with analogies". Queensland University of Technology, 2009. http://eprints.qut.edu.au/30136/.

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Research on analogies in science education has focussed on student interpretation of teacher and textbook analogies, psychological aspects of learning with analogies and structured approaches for teaching with analogies. Few studies have investigated how analogies might be pivotal in students’ growing participation in chemical discourse. To study analogies in this way requires a sociocultural perspective on learning that focuses on ways in which language, signs, symbols and practices mediate participation in chemical discourse. This study reports research findings from a teacher-research study of two analogy-writing activities in a chemistry class. The study began with a theoretical model, Third Space, which informed analyses and interpretation of data. Third Space was operationalized into two sub-constructs called Dialogical Interactions and Hybrid Discourses. The aims of this study were to investigate sociocultural aspects of learning chemistry with analogies in order to identify classroom activities where students generate Dialogical Interactions and Hybrid Discourses, and to refine the operationalization of Third Space. These aims were addressed through three research questions. The research questions were studied through an instrumental case study design. The study was conducted in my Year 11 chemistry class at City State High School for the duration of one Semester. Data were generated through a range of data collection methods and analysed through discourse analysis using the Dialogical Interactions and Hybrid Discourse sub-constructs as coding categories. Results indicated that student interactions differed between analogical activities and mathematical problem-solving activities. Specifically, students drew on discourses other than school chemical discourse to construct analogies and their growing participation in chemical discourse was tracked using the Third Space model as an interpretive lens. Results of this study led to modification of the theoretical model adopted at the beginning of the study to a new model called Merged Discourse. Merged Discourse represents the mutual relationship that formed during analogical activities between the Analog Discourse and the Target Discourse. This model can be used for interpreting and analysing classroom discourse centred on analogical activities from sociocultural perspectives. That is, it can be used to code classroom discourse to reveal students’ growing participation with chemical (or scientific) discourse consistent with sociocultural perspectives on learning.

Mais fontes

Livros sobre o assunto "The learning space":

1

Kommers, Piet A. M., e Madhumita Bhattacharya. The Connected learning space. Chesapeake, VA: Association for the Advancement of Computing in Education, 2009.

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2

Leordeanu, Marius. Unsupervised Learning in Space and Time. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-42128-1.

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3

Sadeghi, Sayed Hadi. Pathology of Learning in Cyber Space. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-91449-7.

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4

Vajpeyi, Kabir. Building as learning aid: Developing school space as learning resource. New Delhi: Vinyās, Centre for Architectural Research & Design, 2005.

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5

McDonald, Frank, e B. M. Evans. Space, place, life: Learning from place 1. Abingdon, Oxon: Routledge, 2011.

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6

Hertzberger, Herman. Space and learning: Lessons in architecture 3. Rotterdam: 010 Publishers, 2008.

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7

Zheng, Yefeng, e Dorin Comaniciu. Marginal Space Learning for Medical Image Analysis. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4939-0600-0.

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8

Marton, Ference. Classroom discourse and the space of learning. Mahwah, NJ: Erlbaum Associates, 2004.

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9

Marriott, S. Self-organising state space decoder for reinforcement learning. Sheffield: University of Sheffield, Dept. of Automatic Control and Systems Engineering, 1995.

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10

Monk, Nicholas. Open-space learning: A study in transdisciplinary pedagogy. London: Bloomsbury, 2011.

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Capítulos de livros sobre o assunto "The learning space":

1

Huang, Ronghuai, J. Michael Spector e Junfeng Yang. "Learning Space Design". In Educational Technology, 149–64. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-6643-7_9.

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Zheng, Yefeng, e Dorin Comaniciu. "Marginal Space Learning". In Marginal Space Learning for Medical Image Analysis, 25–65. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4939-0600-0_2.

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3

Bosch, Antal van den, Bernhard Hengst, John Lloyd, Risto Miikkulainen, Hendrik Blockeel e Hendrik Blockeel. "Hypothesis Space". In Encyclopedia of Machine Learning, 511–13. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_373.

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Bosch, Antal van den, Bernhard Hengst, John Lloyd, Risto Miikkulainen, Hendrik Blockeel e Hendrik Blockeel. "Hypothesis Space". In Encyclopedia of Machine Learning, 513. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_374.

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Utgoff, Paul E., James Cussens, Stefan Kramer, Sanjay Jain, Frank Stephan, Luc De Raedt, Ljupčo Todorovski et al. "Instance Space". In Encyclopedia of Machine Learning, 549. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_408.

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Fürnkranz, Johannes, Philip K. Chan, Susan Craw, Claude Sammut, William Uther, Adwait Ratnaparkhi, Xin Jin et al. "Model Space". In Encyclopedia of Machine Learning, 683. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_552.

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Blockeel, Hendrik, Geoffrey I. Webb, Peter Auer e Geoffrey I. Webb. "Object Space". In Encyclopedia of Machine Learning, 733. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_607.

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Lagoudakis, Michail G., Thomas Zeugmann e Claude Sammut. "Version Space". In Encyclopedia of Machine Learning, 1024–25. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_877.

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Kapitzke, Cushla, e Peter D. Renshaw. "Third Space in Cyberspace". In Dialogic Learning, 45–61. Dordrecht: Springer Netherlands, 2004. http://dx.doi.org/10.1007/1-4020-1931-9_3.

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Falmagne, Jean-Claude, e Jean-Paul Doignon. "Building a Knowledge Space". In Learning Spaces, 297–333. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-01039-2_15.

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Trabalhos de conferências sobre o assunto "The learning space":

1

McCrone, Luke. "Transitional space: learning in the spaces in-between". In Learning Connections 2019: Spaces, People, Practice. University College Cork||National Forum for the Enhancement of Teaching and Learning in Higher Education, 2019. http://dx.doi.org/10.33178/lc2019.14.

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There is increasing evidence, particularly in STEMM education, that traditional didactic transmission lecturing is less effective than more active, student-centred learning (Freeman et al., 2014). This mounting evidence has resulted in institution-wide curriculum review, pedagogic transformation and ongoing space refurbishments at Imperial College London, a research-intensive institution that provides the context for this work. Although active learning is proven to improve cognitive outcomes by supporting ‘students to do meaningful learning activities and think about what they are doing’ (Prince, 2004, p.223), its examination remains largely linked to instructional contexts, with neglect for the self-directed, non-timetabled learning spaces that support a rich learning experience. This instructional emphasis is evident from the capital that Imperial College London, among other institutions, continue to invest into ongoing classroom refurbishments to support curriculum review and innovation. However, it could be argued that these changes to physical infrastructure do not accurately reflect and address the growing self-directed workload that students now contend with. Furthermore, as capital spending on maintaining and modernising university buildings in the UK approaches £3 billion annually (Temple, 2018), these refurbishments are increasingly time- and money-intensive, placing a financial strain on institutions. The assumption that students successfully transition between passive and active learning, between directed and self-directed learning and between formal, timetabled and informal, non-timetabled spaces has meant transitional space being overlooked. By seeking to better understand student engagement with these transitional spaces as physical, curricular and cognitive spatial phenomena, this study is generating evidence for the educational importance of transitional space and using this to better understand active learning. By redesigning underutilised ancillary spaces adjacent to formal lecture spaces at lower cost than lecture theatre refurbishments, students can better self-direct active learning at moments of transition into and out of formal, timetabled spaces.

2

Hughes, Billie, Jim Walters e Barry Kort. "Virtual space learning". In the 1994 ACM symposium. New York, New York, USA: ACM Press, 1994. http://dx.doi.org/10.1145/326619.326949.

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3

Alison, Chan See Mun, Umeda Kyoko e Nozaki Hironari. "Student Learning Space". In the 2019 3rd International Conference. New York, New York, USA: ACM Press, 2019. http://dx.doi.org/10.1145/3345120.3345192.

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4

Cardenas, Jeffery. "Enhancing Higher Education Science & Technology Learning through Exploration". In Space 2006. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2006. http://dx.doi.org/10.2514/6.2006-7301.

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Al-Stouhi, S., C. K. Reddy e D. E. Lanfear. "Label Space Transfer Learning". In 2012 IEEE 24th International Conference on Tools with Artificial Intelligence (ICTAI 2012). IEEE, 2012. http://dx.doi.org/10.1109/ictai.2012.103.

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6

Peters, J., e S. Schaal. "Learning Operational Space Control". In Robotics: Science and Systems 2006. Robotics: Science and Systems Foundation, 2006. http://dx.doi.org/10.15607/rss.2006.ii.033.

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Blanc, Anja Le, Jonathan Bunt, Jim Petch e Yien Kwok. "The virtual learning space". In the tenth international conference. New York, New York, USA: ACM Press, 2005. http://dx.doi.org/10.1145/1050491.1050505.

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Lin, Hsiu-Chin, Matthew Howard e Sethu Vijayakumar. "Learning null space projections". In 2015 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2015. http://dx.doi.org/10.1109/icra.2015.7139551.

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9

Fan, Zhou, Rui Su, Weinan Zhang e Yong Yu. "Hybrid Actor-Critic Reinforcement Learning in Parameterized Action Space". In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/316.

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In this paper we propose a hybrid architecture of actor-critic algorithms for reinforcement learning in parameterized action space, which consists of multiple parallel sub-actor networks to decompose the structured action space into simpler action spaces along with a critic network to guide the training of all sub-actor networks. While this paper is mainly focused on parameterized action space, the proposed architecture, which we call hybrid actor-critic, can be extended for more general action spaces which has a hierarchical structure. We present an instance of the hybrid actor-critic architecture based on proximal policy optimization (PPO), which we refer to as hybrid proximal policy optimization (H-PPO). Our experiments test H-PPO on a collection of tasks with parameterized action space, where H-PPO demonstrates superior performance over previous methods of parameterized action reinforcement learning.

10

van den Dool, Jaco, e Bert van Uffelen. "MUSICAL SAFE SPACE". In International Conference on Education and New Learning Technologies. IATED, 2016. http://dx.doi.org/10.21125/edulearn.2016.1029.

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Relatórios de organizações sobre o assunto "The learning space":

1

Schultz, Alan C. Adapting the Evaluation Space to Improve Global Learning,. Fort Belvoir, VA: Defense Technical Information Center, janeiro de 1991. http://dx.doi.org/10.21236/ada294069.

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2

Koedinger, Kenneth R., Daniel D. Suthers e Kenneth D. Forbus. Component-Based Construction of a Science Learning Space. Fort Belvoir, VA: Defense Technical Information Center, janeiro de 1998. http://dx.doi.org/10.21236/ada638366.

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3

Guttromson, Ross, Stephen Verzi, Christian Jones, Asael Sorensen, Raymond Byrne e Charles Hanley. Grid Stability Using Machine Learning State Space Navigation. Office of Scientific and Technical Information (OSTI), julho de 2019. http://dx.doi.org/10.2172/1762943.

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Boyd, Zachary M., e Joanne Roth Wendelberger. An Integrated Approach to Parameter Learning in Infinite-Dimensional Space. Office of Scientific and Technical Information (OSTI), setembro de 2017. http://dx.doi.org/10.2172/1392846.

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Pearlmutter, Barak A. Learning State Space Trajectories in Recurrent Neural Networks: A preliminary Report. Fort Belvoir, VA: Defense Technical Information Center, julho de 1988. http://dx.doi.org/10.21236/ada219114.

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Qi, Fei, Zhaohui Xia, Gaoyang Tang, Hang Yang, Yu Song, Guangrui Qian, Xiong An, Chunhuan Lin e Guangming Shi. A Graph-based Evolutionary Algorithm for Automated Machine Learning. Web of Open Science, dezembro de 2020. http://dx.doi.org/10.37686/ser.v1i2.77.

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Resumo:

As an emerging field, Automated Machine Learning (AutoML) aims to reduce or eliminate manual operations that require expertise in machine learning. In this paper, a graph-based architecture is employed to represent flexible combinations of ML models, which provides a large searching space compared to tree-based and stacking-based architectures. Based on this, an evolutionary algorithm is proposed to search for the best architecture, where the mutation and heredity operators are the key for architecture evolution. With Bayesian hyper-parameter optimization, the proposed approach can automate the workflow of machine learning. On the PMLB dataset, the proposed approach shows the state-of-the-art performance compared with TPOT, Autostacker, and auto-sklearn. Some of the optimized models are with complex structures which are difficult to obtain in manual design.

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Ahmed AlGarf, Yasmine. From Self-Awareness to Purposeful Employment: Guiding Egyptian youth using arts-based learning. Oxfam IBIS, agosto de 2021. http://dx.doi.org/10.21201/2021.7932.

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Resumo:

Alwan wa Awtar (A&A), a partner of the Youth Participation and Employment (YPE) programme, implements a youth programme supporting young people to shape their prospects through professional and soft skill development, safe learning space and non-formal education. A&A has learned important lessons throughout its journey. A safe learning environment, flexible learning techniques, visual and performing arts in education and participatory management are key approaches for successful youth programmes. During the COVID-19 period, many of the professional development programmes have been delivered online, which was a good example of adaptation to changing circumstances that ensured the sustainability and continued effectiveness of the programme.

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Niyogi, Partha, e Robert C. Berwick. Formalizing Triggers: A Learning Model for Finite Spaces. Fort Belvoir, VA: Defense Technical Information Center, novembro de 1993. http://dx.doi.org/10.21236/ada276776.

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Fukumizu, Kenji, Francis R. Bach e Michael I. Jordan. Dimensionality Reduction for Supervised Learning With Reproducing Kernel Hilbert Spaces. Fort Belvoir, VA: Defense Technical Information Center, maio de 2003. http://dx.doi.org/10.21236/ada446572.

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Lemcke-Kibby, Allison. Utilizing Affinity Spaces and Critical Literacies for Multi-Media Language Learning. Portland State University Library, maio de 2013. http://dx.doi.org/10.15760/honors.26.

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