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Books > Professional & Technical > Electronics & communications engineering > Electronics engineering > Automatic control engineering
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Statistical Learning with Sparsity - The Lasso and Generalizations (Paperback)
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Statistical Learning with Sparsity - The Lasso and Generalizations (Paperback)
Series: Chapman & Hall/CRC Monographs on Statistics and Applied Probability
Expected to ship within 9 - 17 working days
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Discover New Methods for Dealing with High-Dimensional Data A
sparse statistical model has only a small number of nonzero
parameters or weights; therefore, it is much easier to estimate and
interpret than a dense model. Statistical Learning with Sparsity:
The Lasso and Generalizations presents methods that exploit
sparsity to help recover the underlying signal in a set of data.
Top experts in this rapidly evolving field, the authors describe
the lasso for linear regression and a simple coordinate descent
algorithm for its computation. They discuss the application of 1
penalties to generalized linear models and support vector machines,
cover generalized penalties such as the elastic net and group
lasso, and review numerical methods for optimization. They also
present statistical inference methods for fitted (lasso) models,
including the bootstrap, Bayesian methods, and recently developed
approaches. In addition, the book examines matrix decomposition,
sparse multivariate analysis, graphical models, and compressed
sensing. It concludes with a survey of theoretical results for the
lasso. In this age of big data, the number of features measured on
a person or object can be large and might be larger than the number
of observations. This book shows how the sparsity assumption allows
us to tackle these problems and extract useful and reproducible
patterns from big datasets. Data analysts, computer scientists, and
theorists will appreciate this thorough and up-to-date treatment of
sparse statistical modeling.
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