CRC Press, 2014. — 525 p. — ISBN: 9781482241396, e-ISBN: 9781482241402.Regularization, Optimization, Kernels, and Support Vector Machines offers a snapshot of the current state of the art of large-scale machine learning, providing a single multidisciplinary source for the latest research and advances in regularization, sparsity, compressed sensing, convex and large-scale optimization, kernel methods, and support vector machines. Consisting of 21 chapters authored by leading researchers in machine learning, this comprehensive reference:Covers the relationship between support vector machines (SVMs) and the Lasso
Discusses multi-layer SVMs
Explores nonparametric feature selection, basis pursuit methods, and robust compressive sensing
Describes graph-based regularization methods for single- and multi-task learning
Considers regularized methods for dictionary learning and portfolio selection
Addresses non-negative matrix factorization
Examines low-rank matrix and tensor-based models
Presents advanced kernel methods for batch and online machine learning, system identification, domain adaptation, and image processing

Tackles large-scale algorithms including conditional gradient methods, (non-convex) proximal techniques, and stochastic gradient descentRegularization, Optimization, Kernels, and Support Vector Machines is ideal for researchers in machine learning, pattern recognition, data mining, signal processing, statistical learning, and related areas.

CRC出版社，2014年。- 525页--ISBN：9781482241396，e-ISBN：9781482241402.Regularization, Optimization, Kernels, and Support Vector Machines提供了大规模机器学习技术现状的快照，为Regularization、sparsity、compressed sensing、凸和大规模优化、核方法和支持向量机的最新研究和进展提供了一个单一的多学科来源。这本全面的参考书由机器学习领域领先的研究人员撰写的21章组成：涵盖了支持向量机（SVMs）和Lasso之间的关系。

论述了多层SVMs

探讨了非参数特征选择、基数追求方法和鲁棒压缩感应

描述了基于图形的单任务和多任务学习的正则化方法

考虑字典学习和组合选择的正则化方法

解决非负矩阵分解的问题

研究了低等级矩阵和基于张量的模型

介绍了用于批量和在线机器学习、系统识别、领域适应和图像处理的高级内核方法

探讨大规模算法，包括条件梯度方法、（非凸）近似技术和随机梯度下降Regularization, Optimization, Kernels, and Support Vector Machines是机器学习、模式识别、数据挖掘、信号处理、统计学习和相关领域研究人员的