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A graphical model is a statistical model that is represented by a
graph. The factorization properties underlying graphical models
facilitate tractable computation with multivariate distributions,
making the models a valuable tool with a plethora of applications.
Furthermore, directed graphical models allow intuitive causal
interpretations and have become a cornerstone for causal inference.
While there exist a number of excellent books on graphical models,
the field has grown so much that individual authors can hardly
cover its entire scope. Moreover, the field is interdisciplinary by
nature. Through chapters by leading researchers from different
areas, this handbook provides a broad and accessible overview of
the state of the art. Features: Contributions by leading
researchers from a range of disciplines Structured in five parts,
covering foundations, computational aspects, statistical inference,
causal inference, and applications Balanced coverage of concepts,
theory, methods, examples, and applications Chapters can be read
mostly independently, while cross-references highlight connections
The handbook is targeted at a wide audience, including graduate
students, applied researchers, and experts in graphical models.
A graphical model is a statistical model that is represented by a
graph. The factorization properties underlying graphical models
facilitate tractable computation with multivariate distributions,
making the models a valuable tool with a plethora of applications.
Furthermore, directed graphical models allow intuitive causal
interpretations and have become a cornerstone for causal inference.
While there exist a number of excellent books on graphical models,
the field has grown so much that individual authors can hardly
cover its entire scope. Moreover, the field is interdisciplinary by
nature. Through chapters by leading researchers from different
areas, this handbook provides a broad and accessible overview of
the state of the art. Key features: * Contributions by leading
researchers from a range of disciplines * Structured in five parts,
covering foundations, computational aspects, statistical inference,
causal inference, and applications * Balanced coverage of concepts,
theory, methods, examples, and applications * Chapters can be read
mostly independently, while cross-references highlight connections
The handbook is targeted at a wide audience, including graduate
students, applied researchers, and experts in graphical models.
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Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2019, Wurzburg, Germany, September 16-20, 2019, Proceedings, Part III (Paperback, 1st ed. 2020)
Ulf Brefeld, Elisa Fromont, Andreas Hotho, Arno Knobbe, Marloes Maathuis, …
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R1,726
Discovery Miles 17 260
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Ships in 10 - 15 working days
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The three volume proceedings LNAI 11906 - 11908 constitutes the
refereed proceedings of the European Conference on Machine Learning
and Knowledge Discovery in Databases, ECML PKDD 2019, held in
Wurzburg, Germany, in September 2019.The total of 130 regular
papers presented in these volumes was carefully reviewed and
selected from 733 submissions; there are 10 papers in the demo
track. The contributions were organized in topical sections named
as follows: Part I: pattern mining; clustering, anomaly and outlier
detection, and autoencoders; dimensionality reduction and feature
selection; social networks and graphs; decision trees,
interpretability, and causality; strings and streams; privacy and
security; optimization. Part II: supervised learning; multi-label
learning; large-scale learning; deep learning; probabilistic
models; natural language processing. Part III: reinforcement
learning and bandits; ranking; applied data science: computer
vision and explanation; applied data science: healthcare; applied
data science: e-commerce, finance, and advertising; applied data
science: rich data; applied data science: applications; demo track.
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Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2019, Wurzburg, Germany, September 16-20, 2019, Proceedings, Part II (Paperback, 1st ed. 2020)
Ulf Brefeld, Elisa Fromont, Andreas Hotho, Arno Knobbe, Marloes Maathuis, …
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R3,104
Discovery Miles 31 040
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Ships in 10 - 15 working days
|
The three volume proceedings LNAI 11906 - 11908 constitutes the
refereed proceedings of the European Conference on Machine Learning
and Knowledge Discovery in Databases, ECML PKDD 2019, held in
Wurzburg, Germany, in September 2019.The total of 130 regular
papers presented in these volumes was carefully reviewed and
selected from 733 submissions; there are 10 papers in the demo
track. The contributions were organized in topical sections named
as follows: Part I: pattern mining; clustering, anomaly and outlier
detection, and autoencoders; dimensionality reduction and feature
selection; social networks and graphs; decision trees,
interpretability, and causality; strings and streams; privacy and
security; optimization. Part II: supervised learning; multi-label
learning; large-scale learning; deep learning; probabilistic
models; natural language processing. Part III: reinforcement
learning and bandits; ranking; applied data science: computer
vision and explanation; applied data science: healthcare; applied
data science: e-commerce, finance, and advertising; applied data
science: rich data; applied data science: applications; demo track.
Chapter "Incorporating Dependencies in Spectral Kernels for
Gaussian Processes" is available open access under a Creative
Commons Attribution 4.0 International License via
link.springer.com.
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Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2019, Wurzburg, Germany, September 16-20, 2019, Proceedings, Part I (Paperback, 1st ed. 2020)
Ulf Brefeld, Elisa Fromont, Andreas Hotho, Arno Knobbe, Marloes Maathuis, …
|
R3,122
Discovery Miles 31 220
|
Ships in 10 - 15 working days
|
The three volume proceedings LNAI 11906 - 11908 constitutes the
refereed proceedings of the European Conference on Machine Learning
and Knowledge Discovery in Databases, ECML PKDD 2019, held in
Wurzburg, Germany, in September 2019.The total of 130 regular
papers presented in these volumes was carefully reviewed and
selected from 733 submissions; there are 10 papers in the demo
track. The contributions were organized in topical sections named
as follows: Part I: pattern mining; clustering, anomaly and outlier
detection, and autoencoders; dimensionality reduction and feature
selection; social networks and graphs; decision trees,
interpretability, and causality; strings and streams; privacy and
security; optimization. Part II: supervised learning; multi-label
learning; large-scale learning; deep learning; probabilistic
models; natural language processing. Part III: reinforcement
learning and bandits; ranking; applied data science: computer
vision and explanation; applied data science: healthcare; applied
data science: e-commerce, finance, and advertising; applied data
science: rich data; applied data science: applications; demo track.
Chapter "Heavy-tailed Kernels Reveal a Finer Cluster Structure in
t-SNE Visualisations" is available open access under a Creative
Commons Attribution 4.0 International License via
link.springer.com.
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