Regularization, Optimization, Kernels, and Support Vector Machines (Chapman & Hall/Crc Machine Learning & Pattern Recognition Series)

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 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.

《正則化、優化、核函數和支持向量機》提供了大規模機器學習的最新研究和進展的一個多學科來源，為讀者呈現了目前的最新技術。本書由21位領先的機器學習研究人員撰寫，包含以下內容：

- 探討支持向量機（SVM）與Lasso之間的關係
- 討論多層SVM
- 探索非參數特徵選擇、基追蹤方法和魯棒壓縮感知
- 描述基於圖的正則化方法，用於單任務和多任務學習
- 考慮用於字典學習和投資組合選擇的正則化方法
- 討論非負矩陣分解
- 研究低秩矩陣和張量模型
- 提出用於批量和在線機器學習、系統識別、領域適應和圖像處理的高級核方法
- 探討包括條件梯度方法、（非凸）近端技術和隨機梯度下降在內的大規模算法

《正則化、優化、核函數和支持向量機》適合機器學習、模式識別、數據挖掘、信號處理、統計學習等領域的研究人員閱讀。