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Books on the topic 'Distribution learning theory'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Vapnik, Vladimir Naumovich. The Nature of Statistical Learning Theory. Springer New York, 1995.

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2

Vidyasagar, M. Learning and Generalisation: With Applications to Neural Networks. Springer London, 2003.

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3

Gabbay, Dov M. Abductive Reasoning and Learning. Springer Netherlands, 2000.

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4

Knowledge - Its Creation, Distribution and Economic Significance: Knowledge and Knowledge Production. Princeton University Press, 2016.

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5

Machlup, Fritz. Knowledge : Its Creation, Distribution and Economic Significance, Volume I: Knowledge and Knowledge Production. Princeton University Press, 2014.

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6

Machlup, Fritz. Knowledge : Its Creation, Distribution and Economic Significance, Volume I: Knowledge and Knowledge Production. Princeton University Press, 2014.

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7

Machlup, Fritz. Knowledge : Its Creation, Distribution and Economic Significance, Volume III: The Economics of Information and Human Capital. Princeton University Press, 2014.

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8

Machlup, Fritz. Knowledge: Its Creation, Distribution and Economic Significance. Princeton University Press, 2016.

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9

Machlup, Fritz. Knowledge: Its Creation, Distribution and Economic Significance. Princeton University Press, 2014.

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10

Sastry, Kumara, Martin Pelikan, and Erick Cantú-Paz. Scalable Optimization via Probabilistic Modeling: From Algorithms to Applications. Springer, 2010.

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11

(Editor), Martin Pelikan, Kumara Sastry (Editor), and Erick Cantú-Paz (Editor), eds. Scalable Optimization via Probabilistic Modeling: From Algorithms to Applications (Studies in Computational Intelligence). Springer, 2006.

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12

Vanderschraaf, Peter. Playing Fair. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199832194.003.0005.

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Norms requiring individuals to treat their partners fairly can evolve even in populations that lack well-defined identity groups. The emergence of fairness equilibria in the bargaining problem and in Augmented Stag Hunt is analyzed with inductive and evolutionary learning models applied to populations that are not subdivided into preexisting groups. Inductive learning models applied to the bargaining problem yield distributions of equilibrium solutions centered around the egalitarian solution that corresponds to a norm of equal division of benefits. Inductive and evolutionary learning models a

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13

Neapolitan, Richard, and Xia Jiang. The Bayesian Network Story. Edited by Alan Hájek and Christopher Hitchcock. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199607617.013.31.

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Bayesian networks are now among the leading architectures for reasoning with uncertainty in artificial intelligence. This chapter concerns their story, namely what they are, how and why they came into being, how we obtain them, and what they actually represent. First, it is shown that a standard application of Bayes’ Theorem constitutes inference in a two-node Bayesian network. Then more complex Bayesian networks are presented. Next the genesis of Bayesian networks and their relationship to causality is presented. A technique for learning Bayesian networks from data follows. Finally, a discuss

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14

van, José. Education. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190889760.003.0007.

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This chapter investigates how platformization is affecting the idea of education as a common good on both sides of the Atlantic. The growth of online educational platforms has been explosive, in both primary and higher education. Most of these educational platforms are corporately owned, propelled by algorithmic architectures and business models. They have quickly gained millions of users and are altering learning processes and teaching practices; they boost the distribution of online course material, hence impacting curriculums; they influence the administration of schools and universities; a

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15

Tzaros, Chris. Wildlife of the Box-Ironbark Country. CSIRO Publishing, 2005. http://dx.doi.org/10.1071/9780643092211.

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The forests and woodlands of Victoria’s Box-Ironbark Region are one of the most important areas of animal diversity and significance in southern Australia. They provide critical habitat for a diverse array of woodland-dependant animals, including many threatened species such as the Squirrel Glider, Brush-tailed Phascogale, Regent Honeyeater, Swift Parrot, Pink-tailed Worm-lizard and the Woodland Blind Snake.  Wildlife of the Box-Ironbark Country gives a comprehensive overview of the ecology of the box-ironbark habitats and their wildlife. It covers all of the mammals, birds, reptil

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16

Haq, Khadija, ed. Economic Growth with Social Justice. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199474684.001.0001.

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The book traces the evolution of Mahbub ul Haq’s thinking on development, and highlights its impact on global, regional and national policy debates, and relevance to today’s headline events. It situates the origins and significance (both in affecting academic and policy debates) of Haq’s development philosophy focusing on social justice. The introduction to the volume explains Haq’s reasons for moving away from growth-only philosophy to growth with distribution. The four parts of the book show Haq’s contributions to the larger development debate from the 1960s to the 1990s, including on issues

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17

Ruthmann, S. Alex, and Roger Mantie, eds. The Oxford Handbook of Technology and Music Education. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199372133.001.0001.

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Few aspects of daily existence are untouched by technology. The learning and teaching of music is no exception, and arguably has been impacted as much or more than other areas of life. Digital technologies have come to affect music learning and teaching in profound ways, influencing everything from how we create, listen, share, consume, interact, and conceptualize musical practices and the musical experience. For a discipline as entrenched in tradition as music education, this has brought forth myriad views on what does and should constitute music learning and teaching. In order to tease out a

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18

Tzaros, Chris. Wildlife of the Box-Ironbark Country. CSIRO Publishing, 2021. http://dx.doi.org/10.1071/9781486313167.

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Victoria's Box–Ironbark region is one of the most important areas of animal diversity and significance in southern Australia. The forests and woodlands of this region provide critical habitat for a diverse array of woodland-dependent animals, including many threatened and declining species such as the Squirrel Glider, Brush-tailed Phascogale, Regent Honeyeater, Swift Parrot, Pink-tailed Worm-Lizard, Woodland Blind Snake, Tree Goanna and Bibron's Toadlet.  Wildlife of the Box–Ironbark Country gives a comprehensive overview of the ecology of the Box–Ironbark habitats and their wildli

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19

Certoma, Chiara, Susan Noori, and Martin Sondermann, eds. Urban gardening and the struggle for social and spatial justice. Manchester University Press, 2019. http://dx.doi.org/10.7228/manchester/9781526126092.001.0001.

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It is increasingly clear that, alongside the spectacular forms of justice activism, the actually existing just city outcomes from different everyday practices of performative politics that produce transformative trajectories and alternative realities in response to particular injustices in situated contexts. The massive diffusion of urban gardening practices (including allotments, community gardens, guerrilla gardening and the multiple, inventive forms of gardening the city) deserve a special attention as experiential learning and in-becoming responses to spatial politics, able to articulate d

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20

Petchey, Owen L., Andrew P. Beckerman, Natalie Cooper, and Dylan Z. Childs. Insights from Data with R. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780198849810.001.0001.

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Knowledge of how to get useful information from data is essential in the life and environmental sciences. This book provides learners with knowledge, experience, and confidence about how to efficiently and reliably discover useful information from data. The content is developed from first- and second-year undergraduate-level courses taught by the authors. It charts the journey from question, to raw data, to clean and tidy data, to visualizations that provide insights. This journey is presented as a repeatable workflow fit for use with many types of question, study, and data. Readers discover h

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