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1

United States. National Aeronautics and Space Administration., ed. Representation in incremental learning. Moffet Field, Calif: National Aeronautics and Space Administration, Ames Research Center, 1993.

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2

Govea, Alejandro Dizan Vasquez. Incremental Learning for Motion Prediction of Pedestrians and Vehicles. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13642-9.

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3

Hirsh, Haym. Incremental Version-Space Merging: A General Framework for Concept Learning. Boston, MA: Springer US, 1990.

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4

Hirsh, Haym. Incremental Version-Space Merging: A General Framework for Concept Learning. Boston, MA: Springer US, 1990. http://dx.doi.org/10.1007/978-1-4613-1557-5.

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5

Chakraborty, Sanjay, Sk Hafizul Islam, and Debabrata Samanta. Data Classification and Incremental Clustering in Data Mining and Machine Learning. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-93088-2.

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6

Lim, Chee Peng. An incremental adaptive network for on-line, supervised learning and probability estimation. Sheffield: University of Sheffield, Dept. of Automatic Control & Systems Engineering, 1995.

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7

Proietto Salanitri, Federica, Serestina Viriri, Ulaş Bağcı, Pallavi Tiwari, Boqing Gong, Concetto Spampinato, Simone Palazzo, et al., eds. Artificial Intelligence in Pancreatic Disease Detection and Diagnosis, and Personalized Incremental Learning in Medicine. Cham: Springer Nature Switzerland, 2025. http://dx.doi.org/10.1007/978-3-031-73483-0.

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8

Hwang, Francis. Effects of a Curriculum-Based Intervention on the Increments of Stimulus Control for Bidirectional Naming and Student Learning. [New York, N.Y.?]: [publisher not identified], 2021.

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9

Representation in incremental learning. Moffet Field, Calif: National Aeronautics and Space Administration, Ames Research Center, 1993.

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10

Crompton, Deon. Keras Python : Keras Incremental Training: Learning Rate Keras. Independently Published, 2021.

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11

Pang, Shaoning, Nikola Kasabov, and Seiichi Ozawa. Incremental Learning in Intelligent Systems: From Single-Task to Multi-Task Learning. Wiley & Sons, Incorporated, John, 2017.

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12

Govea, Alejandro Dizan Vasquez. Incremental Learning for Motion Prediction of Pedestrians and Vehicles. Springer, 2012.

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13

Incremental Learning For Motion Prediction Of Pedestrians And Vehicles. Springer, 2010.

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14

Govea, Alejandro Dizan Vasquez. Incremental Learning for Motion Prediction of Pedestrians and Vehicles. Springer London, Limited, 2010.

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15

Incremental Version-Space Merging: A General Framework for Concept Learning. Springer, 2011.

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16

Incremental version-space merging: A general framework for concept learning. Boston: Kluwer Academic Publishers, 1990.

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17

Data Classification and Incremental Clustering in Data Mining and Machine Learning. Springer International Publishing AG, 2022.

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18

Cornuejols, Antoine. Training Issues in Incremental Learning: Papers from the 1993 Spring Symposium. AAAI Press, 1993.

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19

Data Classification and Incremental Clustering in Data Mining and Machine Learning. Springer International Publishing AG, 2023.

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20

Za'in, Choiru. Incremental Learning for Large Scale Data Stream Analytics in a Complex Environment. Methuen Educational, Limited, 2023.

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21

Putatunda, Sayan. Practical Machine Learning for Streaming Data with Python: Design, Develop, and Validate Online or Incremental Machine Learning Models in a Streaming Data Context. Apress L. P., 2021.

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22

Hendriks, Carolyn M., Selen A. Ercan, and John Boswell. Mending Democracy. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780198843054.001.0001.

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This book advances the idea of democratic mending in response to the growing problem of disconnections in contemporary democracies. Around the globe vital connections in our democratic systems are wearing thin, especially between citizens and their elected representatives, between citizens in polarized public spheres, and between citizens and their complex governance systems. The wide scale of disrepair in our democratic fabric cannot realistically be patched over through institutional redesign or one-off innovation. Instead this book calls for a more connective and systemic approach to repairing democracies. For reform inspiration the authors engage in a critical dialogue between systems thinking in deliberative democracy and contemporary practices of political participation. They present three rich empirical cases of how everyday actors — citizens, community groups, administrators, and elected officials—are seeking to create and strengthen democratic connections in unpromising or challenging circumstances. The cases uncover the practical and varied work of democratic mending; these are small-scale, incremental interventions aimed at repairing disconnects in different parts of democratic systems. The empirical insights revealed in this book push forward ideas on connectivity in democratic theory and practice. They demonstrate that even in moments of dysfunctional disconnection, considerable learning, adaptation, and improvisation for democratic renewal can emerge. Ultimately, this book pioneers an approach to analysing democratic politics which might spark a ‘connective turn’ in the way scholars and practitioners think about and seek to improve democracy at the large scale.
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23

Levy, Brian. ‘All for Education’—Meeting the Governance Challenge. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198824053.003.0010.

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This chapter explores some implications for education policy of the research laid out in this book. First, it reviews the empirical results on how context, the quality of hierarchical and horizontal governance, and educational outcomes interact. Both the interests of stakeholders and the ideas which they hold vis-à-vis the system’s functioning shape influence whether low-level equilibrium traps take hold, and how they can be overcome. Second, it lays out a two-part policy approach—practical initiatives for incrementally improving hierarchical and horizontal governance, plus a broader reframing of the ideas surrounding how education and other public services should be provided. The overall aim is to initiate a process which proceeds deliberately, and incrementally, while cumulatively building momentum for re-orienting the system as a whole towards learning.
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24

Camargo-Plazas, Pilar, Jennifer Waite, Michaela Sparringa, Martha Whitfield, and Lenora Duhn. Nobody listens, nobody wants to hear you: Access to healthcare/social services for women in Canada. Ludomedia, 2022. http://dx.doi.org/10.36367/ntqr.11.e554.

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In Canada, an unacceptable number of women live below the poverty threshold. Some subgroups of women, such as Indigenous, visible minorities, immigrants and refugees, older adults, and single mothers are more likely to live in poverty, as they face multiple systemic barriers preventing their financial stability. Further, socioeconomic status, employment, gender, and access to healthcare and social services negatively impact women’s well-being and health. Yet little is known about how these factors affect healthcare behaviours and experiences for women living on a low income. Our goal is to describe and understand how gender and income influence access to healthcare and social services for women living on a low income. Methods: Partnered with a not-for-profit organization, we explored the experiences of women living on a low income in Kingston, Canada. Using participatory, art-based research and hermeneutic phenomenological approaches, our data collection methods included photovoice, semi-structured interviews and culture circles. A purposive sample was recruited. Analysis was conducted following the social determinants of health framework by Loppie-Reading and Wien. Results: Participants perceived the healthcare and social services systems as unnecessarily complex, disrespectful, and dismissive–one where they are mere spectators without voice. They do not feel heard. They also identified problematic issues regarding living conditions, housing, and fresh food. Despite these experiences, participants are resilient and optimistic. Implications: Learning from participants has indicated priority issues and potential, pragmatic solutions to begin incremental improvements. Changing system design to enable self-selection of food items is one example. Conclusion: For an individual to feel others view them as unworthy of care, especially if those ‘others’ are the care providers, is ethically and morally distressing–and it certainly does not invite system-use. While our early findings reveal considerable system improvements are required, we are inspired by and can learn from the strength of the participants.
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25

Camargo-Plazas, Pilar, Jennifer Waite, Michaela Sparringa, Martha Whitfield, and Lenora Duhn. Nobody listens, nobody wants to hear you: Access to healthcare/social services for women in Canada. Ludomedia, 2022. http://dx.doi.org/10.36367/ntqr.11.2022.e554.

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Abstract:
In Canada, an unacceptable number of women live below the poverty threshold. Some subgroups of women, such as Indigenous, visible minorities, immigrants and refugees, older adults, and single mothers are more likely to live in poverty, as they face multiple systemic barriers preventing their financial stability. Further, socioeconomic status, employment, gender, and access to healthcare and social services negatively impact women’s well-being and health. Yet little is known about how these factors affect healthcare behaviours and experiences for women living on a low income. Our goal is to describe and understand how gender and income influence access to healthcare and social services for women living on a low income. Methods: Partnered with a not-for-profit organization, we explored the experiences of women living on a low income in Kingston, Canada. Using participatory, art-based research and hermeneutic phenomenological approaches, our data collection methods included photovoice, semi-structured interviews and culture circles. A purposive sample was recruited. Analysis was conducted following the social determinants of health framework by Loppie-Reading and Wien. Results: Participants perceived the healthcare and social services systems as unnecessarily complex, disrespectful, and dismissive–one where they are mere spectators without voice. They do not feel heard. They also identified problematic issues regarding living conditions, housing, and fresh food. Despite these experiences, participants are resilient and optimistic. Implications: Learning from participants has indicated priority issues and potential, pragmatic solutions to begin incremental improvements. Changing system design to enable self-selection of food items is one example. Conclusion: For an individual to feel others view them as unworthy of care, especially if those ‘others’ are the care providers, is ethically and morally distressing–and it certainly does not invite system-use. While our early findings reveal considerable system improvements are required, we are inspired by and can learn from the strength of the participants.
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26

Mundy, Peter. A Neural Networks, Information-Processing Model of Joint Attention and Social-Cognitive Development. Edited by Philip David Zelazo. Oxford University Press, 2013. http://dx.doi.org/10.1093/oxfordhb/9780199958474.013.0010.

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A neural networks approach to the development of joint attention can inform the study of the nature of human social cognition, learning, and symbolic thought process. Joint attention development involves increments in the capacity to engage in simultaneous or parallel processing of information about one’s own attention and the attention of other people. Infant practice with joint attention is both a consequence and an organizer of a distributed and integrated brain network involving frontal and parietal cortical systems. In this chapter I discuss two hypotheses that stem from this model. One is that activation of this distributed network during coordinated attention enhances the depth of information processing and encoding beginning in the first year of life. I also propose that with development joint attention becomes internalized as the capacity to socially coordinate mental attention to internal representations. As this occurs the executive joint attention network makes vital contributions to the development of human social cognition and symbolic thinking.
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