Regularization, Optimization, Kernels, and Support Vector Machines
暫譯: 正則化、優化、核函數與支持向量機

Suykens, Johan A. K., Signoretto, Marco, Argyriou, Andreas

Regularization, Optimization, Kernels, and Support Vector Machines
  • 出版商: CRC
  • 出版日期: 2020-09-30
  • 售價: $2,230
  • 貴賓價: 9.5 折 $2,119
  • 語言: 英文
  • 頁數: 525
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 0367658984
  • ISBN-13: 9780367658984

    • 海外代購書籍(需單獨結帳)

相關主題

商品描述

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.

 

 

商品描述(中文翻譯)

《正則化、優化、核方法與支持向量機》提供了當前大規模機器學習的最新技術概況，成為正則化、稀疏性、壓縮感知、凸優化及大規模優化、核方法和支持向量機等最新研究和進展的單一跨學科來源。該書由機器學習領域的領先研究者撰寫，共包含21章，這本全面的參考書：

- 涵蓋支持向量機（SVM）與Lasso之間的關係
- 討論多層SVM
- 探索非參數特徵選擇、基追求方法和穩健的壓縮感知
- 描述基於圖的正則化方法，用於單任務和多任務學習
- 考慮字典學習和投資組合選擇的正則化方法
- 處理非負矩陣分解
- 檢視低秩矩陣和張量基模型
- 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先進核方法
- 解決包括條件梯度方法、（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、核方法與支持向量機》非常適合機器學習、模式識別、數據挖掘、信號處理、統計學習及相關領域的研究人員。

作者簡介

Johan A.K. Suykens is a professor at Katholieke Universiteit Leuven, Belgium, where he obtained a degree in electro-mechanical engineering and a Ph.D in applied sciences. He has been a visiting postdoctoral researcher at the University of California, Berkeley, USA, and a postdoctoral researcher with the Fonds Wetenschappelijk Onderzoek - Vlaanderen, Belgium. A senior IEEE member, he has co/authored and edited several books; received many prestigious awards; directed, co/organized, and co/chaired numerous international conferences; and served as associate editor for the IEEE Transactions on Circuits and Systems and the IEEE Transactions on Neural Networks.

 

 

 

 

 

 

 

Marco Signoretto is currently a visiting lecturer at the Centre for Computational Statistics and Machine Learning (CSML), University College London, UK, in the framework of a postdoctoral fellowship with the Belgian Fund for Scientific Research (FWO). He holds a Ph.D in mathematical engineering from Katholieke Universiteit Leuven, Belgium; a degree in electronic engineering (Laurea Magistralis) from the University of Padova, Italy; and an M.Sc in methods for management of complex systems from the University of Pavia, Italy. His research interests include practical and theoretical aspects of mathematical modeling of structured data, with special focus on multivariate time-series, networks, and dynamical systems. His current work deals with methods based on (convex) optimization, structure-inducing penalties, and spectral regularization.

 

 

 

 

 

 

 

 

 

Andreas Argyriou has received degrees in computer science from the Massachusetts Institute of Technology, Cambridge, USA, and a Ph.D in computer science from University College London (UCL), UK. The topic of his Ph.D work has been on machine learning methodologies integrating multiple tasks and data sources. He has held postdoctoral and research faculty positions at UCL; Toyota Technological Institute at Chicago, Illinois, USA; and Katholieke Universiteit Leuven, Belgium. He is currently serving an RBUCE-UP fellowship at École Centrale Paris, France. His current interests are in the areas of kernel methods, multitask learning, compressed sensing, and convex optimization methods.

 

 

作者簡介(中文翻譯)

約翰·A·K·蘇肯斯是比利時魯汀大學的教授，獲得了機電工程學位和應用科學博士學位。他曾在美國加州大學伯克利分校擔任訪問博士後研究員，並在比利時科學研究基金會（Fonds Wetenschappelijk Onderzoek - Vlaanderen）擔任博士後研究員。作為資深IEEE會員，他共同撰寫和編輯了多本書籍，獲得了許多著名獎項，指導、共同組織和共同主持了多個國際會議，並擔任《IEEE電路與系統學報》（IEEE Transactions on Circuits and Systems）和《IEEE神經網絡學報》（IEEE Transactions on Neural Networks）的副編輯。








馬可·西尼奧雷托目前是英國倫敦大學學院計算統計與機器學習中心（Centre for Computational Statistics and Machine Learning, CSML）的訪問講師，這是比利時科學研究基金（FWO）提供的博士後獎學金的一部分。他擁有比利時魯汀大學的數學工程博士學位；意大利帕多瓦大學的電子工程學位（Laurea Magistralis）；以及意大利帕維亞大學的複雜系統管理方法碩士學位（M.Sc）。他的研究興趣包括結構化數據的數學建模的實踐和理論方面，特別關注多變量時間序列、網絡和動態系統。他目前的工作涉及基於（凸）優化、結構誘導懲罰和譜正則化的方法。











安德烈亞斯·阿基里尤在美國麻省理工學院獲得計算機科學學位，並在英國倫敦大學學院（UCL）獲得計算機科學博士學位。他的博士研究主題是整合多任務和數據來源的機器學習方法。他曾在UCL、位於美國伊利諾伊州的豐田技術研究所以及比利時魯汀大學擔任博士後和研究教職。他目前在法國巴黎中央學院（École Centrale Paris）擔任RBUCE-UP獎學金的研究員。他目前的研究興趣包括核方法、多任務學習、壓縮感知和凸優化方法。

類似商品