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Optimization with Sparsity-Inducing Penalties

. Optimization with Sparsity-Inducing Penalties presents optimization tools and techniques dedicated to such sparsity-inducing penalties from a general;

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Statistical Learning with Sparsity

or weights; therefore, it is much easier to estimate and interpret than a dense model. Statistical Learning with Sparsity: The Lasso and;

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Statistical Learning With Sparsity

weights; therefore, it is much easier to estimate and interpret than a dense model. Statistical Learning with Sparsity: The Lasso and;

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Regularized Optimization Methods for Reconstruction and Modeling in Computer Graphics

The field of computer graphics deals with virtual representations of the real world. These can be obtained either through reconstruction;

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Sparse Optimization Theory and Methods

science. Sparse Optimization Theory and Methods presents the state of the art in theory and algorithms for signal recovery under the sparsity;

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Sparse Optimization Theory and Methods

realistic results in these fields. The mathematical optimization plays a fundamentally important role in the development of these results and acts;

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Sparse Modeling

interpretability of a predictive model is essential. Sparsity can also dramatically improve the cost efficiency of signal processing. Sparse Modeling;

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Sparse Modeling

optimization problems involving commonly used sparsity-enforcing tools, presents essential theoretical results, and discusses several state-of-the-art;

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Handbook of Convex Optimization Methods in Imaging Science

This book covers recent advances in image processing and imaging sciences from an optimization viewpoint, especially convex optimization;

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Mathematical Theories of Machine Learning - Theory and Applications

authors introduce an novel VAR model with Elastic-Net regularization and its equivalent Bayesian model allowing for both a stable sparsity and a;

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Variational Bayesian Learning Theory

suggests how this theory can be applied in practice. The authors begin by developing a basic framework with a focus on conjugacy, which enables the;

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Advanced Sparsity-Driven Models and Methods for Radar Applications

-level block sparsity model for multi-channel radar signals; parametric sparse representation for radar imaging with model uncertainty; Poisson;

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Regularization, Optimization, Kernels, and Support Vector Machines

Regularization, Optimization, Kernels, and Support Vector Machines offers a snapshot of the current state of the art of large-scale machine;

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Regularization, Optimization, Kernels, and Support Vector Machines

Regularization, Optimization, Kernels, and Support Vector Machines offers a snapshot of the current state of the art of large-scale machine;

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Community Penalties

a major change with the establishment of the National Probation Service for England and Wales. Community Penalties: change and challenges;

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Community Penalties

a major change with the establishment of the National Probation Service for England and Wales. Community Penalties: change and challenges;

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Sparsity Based Multipath Exploitation for Through the Wall Radar Imaging

This thesis reports on sparsity-based multipath exploitation methods for through-the-wall radar imaging. Multipath creates ambiguities;

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Financial Signal Processing and Machine Learning

The modern financial industry has been required to deal with large and diverse portfolios in a variety of asset classes often with limited;

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A Column Generation Approach For Stochastic Optimization Problems

methods that take uncertainty into account can lead to significant reductions in cost. This book investigates the use of stochastic optimization;

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Waveform Diversity

any online entitlements included with the product. Cutting-edge transmitter and receiver waveform design techniquesOptimum design;

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Reforming Community Penalties

This book sets out to explore the role of community penalties in sentencing, arguing that the absence of a strong intellectual framework or;

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Reforming Community Penalties

This book sets out to explore the role of community penalties in sentencing, arguing that the absence of a strong intellectual framework or;

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Molecular Imaging in Nano MRI

, to consider the reconstruction of sparse images. Many works have leveraged the assumption of sparsity in order to achieve an improved;

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