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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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Sparse Estimation with Math and R

in machine learning by the same author. Other titles are: - Statistical Learning with Math and R (https://www.springer.com/gp/book/9789811575679;

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Sparse Estimation with Math and Python

learning by the same Author. Other titles are: Statistical Learning with Math and R (https://www.springer.com/gp/book/9789811575679;

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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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Sparse Modeling

up to speed on the latest sparsity-related developments and will motivate you to continue learning about the field. The authors first;

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Sparse Modeling

book gets you up to speed on the latest sparsity-related developments and will motivate you to continue learning about the field. The;

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Semi-Supervised Learning and Domain Adaptation in Natural Language Processing

biased. This book introduces extensions of supervised learning algorithms to cope with data sparsity and different kinds of sampling bias;

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Introduction To Sparse Stochastic Processes

and researchers with an interest in signal/image processing, compressed sensing, approximation theory, machine learning, or statistics.;

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Regularization, Optimization, Kernels, and Support Vector Machines

learning, providing a single multidisciplinary source for the latest research and advances in regularization, sparsity, compressed sensing, convex;

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Regularization, Optimization, Kernels, and Support Vector Machines

learning, providing a single multidisciplinary source for the latest research and advances in regularization, sparsity, compressed sensing, convex;

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Financial Signal Processing and Machine Learning

construction, monitoring, and post-trade analysis problems. * Presents portfolio theory, sparse learning and compressed sensing, sparsity methods for;

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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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Sparse Optimization Theory and Methods

as the mainstream numerical algorithms for the sparsity-seeking problems arising from big-data processing, compressed sensing, statistical;

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Sparse Optimization Theory and Methods

as the mainstream numerical algorithms for the sparsity-seeking problems arising from big-data processing, compressed sensing, statistical;

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Learning Approaches in Signal Processing

Coupled with machine learning, the use of signal processing techniques for big data analysis, Internet of things, smart cities, security;

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Compressed Sensing for Engineers

Compressed Sensing (CS) in theory deals with the problem of recovering a sparse signal from an under-determined system of linear equations;

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Compressed Sensing for Engineers

Compressed Sensing (CS) in theory deals with the problem of recovering a sparse signal from an under-determined system of linear equations;

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Statistical Learning from a Regression Perspective

This textbook considers statistical learning applications when interest centers on the conditional distribution of a response variable;

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Variational Bayesian Learning Theory

Variational Bayesian learning is one of the most popular methods in machine learning. Designed for researchers and graduate students;

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Machine Learning

covering both pillars of supervised learning, namely regression and classification. The book starts with the basics, including mean square, least;

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An Elementary Introduction to Statistical Learning Theory

A thought-provoking look at statistical learning theory and its role in understanding human learning and inductive reasoning A joint;

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Machine Learning for Knowledge Discovery with R

Machine Learning for Knowledge Discovery with R contains methodologies and examples for statistical modelling, inference, and prediction;

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Probabilistic Foundations of Statistical Network Analysis

in contemporary data science and machine learning applications. The book rewards readers with a clear and intuitive understanding of the subtle interplay;

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Probabilistic Foundations of Statistical Network Analysis

in contemporary data science and machine learning applications. The book rewards readers with a clear and intuitive understanding of the subtle interplay;

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High-Dimensional Covariance Estimation

graduate-level courses in multivariate analysis, covariance estimation, statistical learning, and high-dimensional data analysis.;

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Statistical Learning and Data Sciences

This book constitutes the refereed proceedings of the Third International Symposium on Statistical Learning and Data Sciences, SLDS 2015;

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