稀疏统计学习

Trevor Hastie

评分 暂无

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

稀疏统计学习及其应用

Trevor Hastie

评分 8.4分

稀疏统计模型只具有少数非零参数或权重，经典地体现了化繁为简的理念，因而广泛应用于诸多领域。本书就稀疏性统计学习做出总结，以 lasso方法为中心，层层推进，逐渐囊括其他方法，深入探讨诸多稀疏性问题的求解和应用；不仅包含大量的例子和清晰的图表，还附有文献注释和课后练习，是深入学习统计学知识的参考。 本书适合算法、统计学和机器学习专业人士。 Trevor

统计学习要素（第2版）

Trevor Hastie

评分 7.2分

三位统计学家高屋建瓴，面向非统计专业的读者介绍重要的统计学概念，而非纯数学理论 借助于一个通用概念框架，描述多个学科的重要思想，比如医学、生物学、金融学和营销 《统计学习要素》（第2版）包含人工智能中用到的许多代表性主题，比如图模型、随机森林、集成方法、Lasso最小角度回归和路径算法、非负矩阵分解和频谱聚类。此外，还用一章篇幅来介绍“宽”数据（p大于

The Elements of Statistical Learning

Trevor Hastie

评分 9.5分

During the past decade there has been an explosion in computation and information technology. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and m

Statistical Learning with Sparsity

Trevor Hastie

评分 9.0分

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: T

统计学习基础(第2版)(英文)

Trevor Hastie

评分 9.3分

This book is our attempt to bring together many of the important new ideas in learning, and explain them in a statistical framework. While some mathematical details are needed, we emphasize th