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Sparse models are particularly useful in scientific applications,
such as biomarker discovery in genetic or neuroimaging data, where
the interpretability of a predictive model is essential. Sparsity
can also dramatically improve the cost efficiency of signal
processing. Sparse Modeling: Theory, Algorithms, and Applications
provides an introduction to the growing field of sparse modeling,
including application examples, problem formulations that yield
sparse solutions, algorithms for finding such solutions, and recent
theoretical results on sparse recovery. The book gets you up to
speed on the latest sparsity-related developments and will motivate
you to continue learning about the field. The authors first present
motivating examples and a high-level survey of key recent
developments in sparse modeling. The book then describes
optimization problems involving commonly used sparsity-enforcing
tools, presents essential theoretical results, and discusses
several state-of-the-art algorithms for finding sparse solutions.
The authors go on to address a variety of sparse recovery problems
that extend the basic formulation to more sophisticated forms of
structured sparsity and to different loss functions. They also
examine a particular class of sparse graphical models and cover
dictionary learning and sparse matrix factorizations.
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Machine Learning and Interpretation in Neuroimaging - International Workshop, MLINI 2011, Held at NIPS 2011, Sierra Nevada, Spain, December 16-17, 2011, Revised Selected and Invited Contributions (Paperback, 2012 ed.)
Georg Langs, Irina Rish, Moritz Grosse-Wentrup, Brian Murphy
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R1,429
Discovery Miles 14 290
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Ships in 10 - 15 working days
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Brain imaging brings together the technology, methodology, research
questions and approaches of a wide range of scientific fields
including physics, statistics, computer science, neuroscience,
biology, and engineering. Thus, methodological and technological
advances that enable us to obtain measurements, examine
relationships across observations, and link these data to
neuroscientific hypotheses happen in a highly interdisciplinary
environment. The dynamic field of machine learning with its modern
approach to data mining provides many relevant approaches for
neuroscience and enables the exploration of open questions. This
state-of-the-art survey offers a collection of papers from the
Workshop on Machine Learning and Interpretation in Neuroimaging,
MLINI 2011, held at the 25th Annual Conference on Neural
Information Processing, NIPS 2011, in the Sierra Nevada, Spain, in
December 2011. Additionally, invited speakers agreed to contribute
reviews on various aspects of the field, adding breadth and
perspective to the volume. The 32 revised papers were carefully
selected from 48 submissions. At the interface between machine
learning and neuroimaging the papers aim at shedding some light on
the state of the art in this interdisciplinary field. They are
organized in topical sections on coding and decoding, neuroscience,
dynamcis, connectivity, and probabilistic models and machine
learning.
Sparse models are particularly useful in scientific applications,
such as biomarker discovery in genetic or neuroimaging data, where
the interpretability of a predictive model is essential. Sparsity
can also dramatically improve the cost efficiency of signal
processing. Sparse Modeling: Theory, Algorithms, and Applications
provides an introduction to the growing field of sparse modeling,
including application examples, problem formulations that yield
sparse solutions, algorithms for finding such solutions, and recent
theoretical results on sparse recovery. The book gets you up to
speed on the latest sparsity-related developments and will motivate
you to continue learning about the field. The authors first present
motivating examples and a high-level survey of key recent
developments in sparse modeling. The book then describes
optimization problems involving commonly used sparsity-enforcing
tools, presents essential theoretical results, and discusses
several state-of-the-art algorithms for finding sparse solutions.
The authors go on to address a variety of sparse recovery problems
that extend the basic formulation to more sophisticated forms of
structured sparsity and to different loss functions. They also
examine a particular class of sparse graphical models and cover
dictionary learning and sparse matrix factorizations.
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Machine Learning and Interpretation in Neuroimaging - 4th International Workshop, MLINI 2014, Held at NIPS 2014, Montreal, QC, Canada, December 13, 2014, Revised Selected Papers (Paperback, 1st ed. 2016)
Irina Rish, Georg Langs, Leila Wehbe, Guillermo Cecchi, Kai-min Kevin Chang, …
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R1,680
Discovery Miles 16 800
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Ships in 10 - 15 working days
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This book constitutes the revised selected papers from the 4th
International Workshop on Machine Learning and Interpretation in
Neuroimaging, MLINI 2014, held in Montreal, QC, Canada, in December
2014 as a satellite event of the 11th annual conference on Neural
Information Processing Systems, NIPS 2014. The 10 MLINI 2014 papers
presented in this volume were carefully reviewed and selected from
17 submissions. They were organized in topical sections named:
networks and decoding; speech; clinics and cognition; and causality
and time-series. In addition, the book contains the 3 best papers
presented at MLINI 2013.
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