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 des...(展开全部)

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 des...(展开全部)

This is a quote from baseball pitcher Dizzy Dean， who played in the major leagues from 1930 to 1947. How the world has changed in the 75 or so years since that time! Now large quantities of data are collected and mined in nearly every area of science， entertainment， business， and industry. Medical scientists study the genomes of patients to choose the best treatments， to learn ...(展开全部)

This is a quote from baseball pitcher Dizzy Dean， who played in the major leagues from 1930 to 1947. How the world has changed in the 75 or so years since that time! Now large quantities of data are collected and mined in nearly every area of science， entertainment， business， and industry. Medical scientists study the genomes of patients to choose the best treatments， to learn ...(展开全部)

During the past decade there has been an explosion in computation and information technology. With it has come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and...(展开全部)

During the past decade there has been an explosion in computation and information technology. With it has come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and...(展开全部)

Sure to be influential, this book lays the foundations for the use of algebraic geometry in statistical learning theory. Many widely used statistical models and learning machines applied to information science have a parameter space that is singular: mixture models, neural networks, HMMs, Bayesian networks, and stochastic context-free grammars are major examples. Algebraic geom...(展开全部)

Sure to be influential, this book lays the foundations for the use of algebraic geometry in statistical learning theory. Many widely used statistical models and learning machines applied to information science have a parameter space that is singular: mixture models, neural networks, HMMs, Bayesian networks, and stochastic context-free grammars are major examples. Algebraic geom...(展开全部)