Accelerated Optimization for Machine Learning: First-Order Algorithms
暫譯: 機器學習的加速優化：一階演算法

Lin, Zhouchen, Li, Huan, Fang, Cong

Accelerated Optimization for Machine Learning: First-Order Algorithms
  • 出版商: Springer
  • 出版日期: 2020-05-30
  • 售價: $6,780
  • 貴賓價: 9.5 折 $6,441
  • 語言: 英文
  • 頁數: 273
  • 裝訂: Hardcover - also called cloth, retail trade, or trade
  • ISBN: 9811529094
  • ISBN-13: 9789811529092
  • 相關分類: Machine Learning、Algorithms-data-structures

    • 海外代購書籍(需單獨結帳)

相關主題

商品描述

This book on optimization includes forewords by Michael I. Jordan, Zongben Xu and Zhi-Quan Luo. Machine learning relies heavily on optimization to solve problems with its learning models, and first-order optimization algorithms are the mainstream approaches. The acceleration of first-order optimization algorithms is crucial for the efficiency of machine learning.

Written by leading experts in the field, this book provides a comprehensive introduction to, and state-of-the-art review of accelerated first-order optimization algorithms for machine learning. It discusses a variety of methods, including deterministic and stochastic algorithms, where the algorithms can be synchronous or asynchronous, for unconstrained and constrained problems, which can be convex or non-convex. Offering a rich blend of ideas, theories and proofs, the book is up-to-date and self-contained. It is an excellent reference resource for users who are seeking faster optimization algorithms, as well as for graduate students and researchers wanting to grasp the frontiers of optimization in machine learning in a short time.

商品描述(中文翻譯)

這本關於優化的書籍包含了 Michael I. Jordan、徐宗本和羅志權的前言。機器學習在解決其學習模型的問題時，極度依賴優化，而一階優化算法是主流的方法。一階優化算法的加速對於機器學習的效率至關重要。

本書由該領域的領先專家撰寫，提供了對於加速一階優化算法在機器學習中的全面介紹和最先進的回顧。它討論了各種方法，包括確定性和隨機算法，這些算法可以是同步或異步的，適用於無約束和有約束的問題，這些問題可以是凸的或非凸的。本書提供了豐富的思想、理論和證明，內容更新且自成體系。對於尋求更快優化算法的使用者，以及希望在短時間內掌握機器學習中優化前沿的研究生和研究人員來說，這是一本極好的參考資源。

作者簡介

Zhouchen Lin is a leading expert in the fields of machine learning and computer vision. He is currently a Professor at the Key Laboratory of Machine Perception (Ministry of Education), School of EECS, Peking University. He served as an area chair for several prestigious conferences, including CVPR, ICCV, ICML, NIPS, AAAI and IJCAI. He is an associate editor of the IEEE Transactions on Pattern Analysis and Machine Intelligence and the International Journal of Computer Vision. He is a Fellow of IAPR and IEEE.

Huan Li received his Ph.D. degree in machine learning from Peking University in 2019. He is currently an Assistant Professor at the College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics. His current research interests include optimization and machine learning.

Cong Fang received his Ph.D. degree from Peking University in 2019. He is currently a Postdoctoral Researcher at Princeton University. His research interests include machine learning and optimization.


作者簡介(中文翻譯)

周晨林是機器學習和計算機視覺領域的領先專家。他目前是北京大學電子工程與計算機科學學院機器感知重點實驗室（教育部）的教授。他曾擔任多個知名會議的區域主席，包括 CVPR、ICCV、ICML、NIPS、AAAI 和 IJCAI。他是《IEEE模式分析與機器智慧學報》和《國際計算機視覺期刊》的副編輯。他是 IAPR 和 IEEE 的會士。

李煥於2019年在北京大學獲得機器學習博士學位。他目前是南京航空航天大學計算機科學與技術學院的助理教授。他目前的研究興趣包括優化和機器學習。

方聰於2019年在北京大學獲得博士學位。他目前是普林斯頓大學的博士後研究員。他的研究興趣包括機器學習和優化。

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