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Sparse modeling is an important issue in many applications of machine learning and statistics where the main objective is discovering predictive patterns in data to enhance understanding of underlying physical, biological, and other natural processes. This book surveys recent advances in statistics, machine learning, and signal processing related to sparse modeling. It provides a comprehensive introduction to recent developments in sparse modeling research, including the theoretical basis for sparse modeling, algorithmic approaches, and applications to computational biology, medicine, neuroscience, graphical model selection, and compressed sensing.
When you click on links to various merchants on this site and make a purchase, this can result in this site earning a commission. Affiliate programs and affiliations include, but are not limited to, the eBay Partner Network.