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Computational Topology in Image Context - 4th International Workshop, CTIC 2012, Bertinoro, Italy, May 28-30, 2012, Proceedings (Paperback)
Massimo Ferri, Patrizio Frosini, Claudia Landi, Andrea Cerri, Barbara Di Fabio
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R1,747
Discovery Miles 17 470
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Ships in 18 - 22 working days
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This book constitutes the proceedings of the 4th International
Workshop on Computational Topology in Image Context, CTIC 2012,
held in Bertinoro, Italy, in May 2012. The 16 papers presented in
this volume were carefully reviewed and selected for inclusion in
this book. They focus on the topology and computation in image
context. The workshop is devoted to computational methods using
topology for the analysis and comparison of images. The involved
research fields comprise computational topology and geometry,
discrete topology and geometry, geometrical modeling, algebraic
topology for image applications, and any other field involving a
geometric-topological approach to image processing.
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Towards Integrative Machine Learning and Knowledge Extraction - BIRS Workshop, Banff, AB, Canada, July 24-26, 2015, Revised Selected Papers (Paperback, 1st ed. 2017)
Andreas Holzinger, Randy Goebel, Massimo Ferri, Vasile Palade
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R1,408
Discovery Miles 14 080
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Ships in 18 - 22 working days
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The BIRS Workshop "Advances in Interactive Knowledge Discovery and
Data Mining in Complex and Big Data Sets" (15w2181), held in July
2015 in Banff, Canada, was dedicated to stimulating a cross-domain
integrative machine-learning approach and appraisal of "hot topics"
toward tackling the grand challenge of reaching a level of useful
and useable computational intelligence with a focus on real-world
problems, such as in the health domain. This encompasses learning
from prior data, extracting and discovering knowledge, generalizing
the results, fighting the curse of dimensionality, and ultimately
disentangling the underlying explanatory factors in complex data,
i.e., to make sense of data within the context of the application
domain. The workshop aimed to contribute advancements in promising
novel areas such as at the intersection of machine learning and
topological data analysis. History has shown that most often the
overlapping areas at intersections of seemingly disparate fields
are key for the stimulation of new insights and further advances.
This is particularly true for the extremely broad field of machine
learning.
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