• 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 descent

Regularization, Optimization, Kernels, and Support Vector Machines is ideal for researchers in machine learning, pattern recognition, data mining, signal processing, statistical learning, and related areas.