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Showing 1 - 7 of
7 matches in All Departments
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Artificial Neural Networks in Pattern Recognition - 8th IAPR TC3 Workshop, ANNPR 2018, Siena, Italy, September 19-21, 2018, Proceedings (Paperback, 1st ed. 2018)
Luca Pancioni, Friedhelm Schwenker, Edmondo Trentin
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R2,360
Discovery Miles 23 600
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Ships in 10 - 15 working days
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This book constitutes the refereed proceedings of the 8th IAPR TC3
International Workshop on Artificial Neural Networks in Pattern
Recognition, ANNPR 2018, held in Siena, Italy, in September 2018.
The 29 revised full papers presented together with 2 invited papers
were carefully reviewed and selected from 35 submissions. The
papers present and discuss the latest research in all areas of
neural network- and machine learning-based pattern recognition.
They are organized in two sections: learning algorithms and
architectures, and applications. Chapter "Bounded Rational
Decision-Making with Adaptive Neural Network Priors" is available
open access under a Creative Commons Attribution 4.0 International
License via link.springer.com.
This book presents an exciting new synthesis of directed and
undirected, discrete and continuous graphical models. Combining
elements of Bayesian networks and Markov random fields, the newly
introduced hybrid random fields are an interesting approach to get
the best of both these worlds, with an added promise of modularity
and scalability. The authors have written an enjoyable
book---rigorous in the treatment of the mathematical background,
but also enlivened by interesting and original historical and
philosophical perspectives. -- Manfred Jaeger, Aalborg Universitet
The book not only marks an effective direction of investigation
with significant experimental advances, but it is also---and
perhaps primarily---a guide for the reader through an original trip
in the space of probabilistic modeling. While digesting the book,
one is enriched with a very open view of the field, with full of
stimulating connections. [...] Everyone specifically interested in
Bayesian networks and Markov random fields should not miss it. --
Marco Gori, Universita degli Studi di Siena Graphical models are
sometimes regarded---incorrectly---as an impractical approach to
machine learning, assuming that they only work well for
low-dimensional applications and discrete-valued domains. While
guiding the reader through the major achievements of this research
area in a technically detailed yet accessible way, the book is
concerned with the presentation and thorough (mathematical and
experimental) investigation of a novel paradigm for probabilistic
graphical modeling, the hybrid random field. This model subsumes
and extends both Bayesian networks and Markov random fields.
Moreover, it comes with well-defined learning algorithms, both for
discrete and continuous-valued domains, which fit the needs of
real-world applications involving large-scale, high-dimensional
data.
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Artificial Neural Networks in Pattern Recognition - 5th INNS IAPR TC 3 GIRPR Workshop, ANNPR 2012, Trento, Italy, September 17-19, 2012, Proceedings (Paperback, 2012 ed.)
Nadia Mana, Friedhelm Schwenker, Edmondo Trentin
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R1,429
Discovery Miles 14 290
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Ships in 10 - 15 working days
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This book constitutes the refereed proceedings of the 5th INNS IAPR
TC3 GIRPR International Workshop on Artificial Neural Networks in
Pattern Recognition, ANNPR 2012, held in Trento, Italy, in
September 2012. The 21 revised full papers presented were carefully
reviewed and selected for inclusion in this volume. They cover a
large range of topics in the field of neural network- and machine
learning-based pattern recognition presenting and discussing the
latest research, results, and ideas in these areas.
This book constitutes thoroughly refereed revised selected papers
from the First IAPR TC3 Workshop on Partially Supervised Learning,
PSL 2011, held in Ulm, Germany, in September 2011. The 14 papers
presented in this volume were carefully reviewed and selected for
inclusion in the book, which also includes 3 invited talks. PSL
2011 dealt with methodological issues as well as real-world
applications of PSL. The main methodological issues were:
combination of supervised and unsupervised learning; diffusion
learning; semi-supervised classification, regression, and
clustering; learning with deep architectures; active learning; PSL
with vague, fuzzy, or uncertain teaching signals; learning, or
statistical pattern recognition; and PSL in cognitive systems.
Applications of PSL included: image and signal processing;
multi-modal information processing; sensor/information fusion;
human computer interaction; data mining and Web mining; forensic
anthropology; and bioinformatics.
This book presents an exciting new synthesis of directed and
undirected, discrete and continuous graphical models. Combining
elements of Bayesian networks and Markov random fields, the newly
introduced hybrid random fields are an interesting approach to get
the best of both these worlds, with an added promise of modularity
and scalability. The authors have written an enjoyable
book---rigorous in the treatment of the mathematical background,
but also enlivened by interesting and original historical and
philosophical perspectives. -- Manfred Jaeger, Aalborg Universitet
The book not only marks an effective direction of investigation
with significant experimental advances, but it is also---and
perhaps primarily---a guide for the reader through an original trip
in the space of probabilistic modeling. While digesting the book,
one is enriched with a very open view of the field, with full of
stimulating connections. ...] Everyone specifically interested in
Bayesian networks and Markov random fields should not miss it. --
Marco Gori, Universita degli Studi di Siena Graphical models are
sometimes regarded---incorrectly---as an impractical approach to
machine learning, assuming that they only work well for
low-dimensional applications and discrete-valued domains. While
guiding the reader through the major achievements of this research
area in a technically detailed yet accessible way, the book is
concerned with the presentation and thorough (mathematical and
experimental) investigation of a novel paradigm for probabilistic
graphical modeling, the hybrid random field. This model subsumes
and extends both Bayesian networks and Markov random fields.
Moreover, it comes with well-defined learning algorithms, both for
discrete and continuous-valued domains, which fit the needs of
real-world applications involving large-scale, high-dimensional
data.
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Artificial Neural Networks in Pattern Recognition - 10th IAPR TC3 Workshop, ANNPR 2022, Dubai, United Arab Emirates, November 24-26, 2022, Proceedings (Paperback, 1st ed. 2023)
Neamat El Gayar, Edmondo Trentin, Mirco Ravanelli, Hazem Abbas
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R1,914
Discovery Miles 19 140
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Ships in 10 - 15 working days
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This book constitutes the refereed proceedings of the 10th IAPR TC3
International Workshop on Artificial Neural Networks in Pattern
Recognition, ANNPR 2022, held in Dubai, UAE, in November 2022. The
16 revised full papers presented were carefully reviewed and
selected from 24 submissions. The conference presents papers on
subject such as pattern recognition and machine learning based on
artificial neural networks.
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Artificial Neural Networks in Pattern Recognition - 7th IAPR TC3 Workshop, ANNPR 2016, Ulm, Germany, September 28-30, 2016, Proceedings (Paperback, 1st ed. 2016)
Friedhelm Schwenker, Hazem M. Abbas, Neamat El Gayar, Edmondo Trentin
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R2,210
Discovery Miles 22 100
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Ships in 10 - 15 working days
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This book constitutes the refereed proceedings of the 7th IAPR TC3
International Workshop on Artificial Neural Networks in Pattern
Recognition, ANNPR 2016, held in Ulm, Germany, in September 2016.
The 25 revised full papers presented together with 2 invited papers
were carefully reviewed and selected from 32 submissions for
inclusion in this volume. The workshop will act as a major forum
for international researchers and practitioners working in all
areas of neural network- and machine learning-based pattern
recognition to present and discuss the latest research, results,
and ideas in these areas.
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