Empirical Approach to Machine Learning
暫譯: 機器學習的實證方法

Angelov, Plamen P., Gu, Xiaowei

Empirical Approach to Machine Learning
  • 出版商: Springer
  • 出版日期: 2019-12-10
  • 售價: $7,920
  • 貴賓價: 9.5 折 $7,524
  • 語言: 英文
  • 頁數: 423
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 3030132099
  • ISBN-13: 9783030132095
  • 相關分類: Machine Learning

    • 海外代購書籍(需單獨結帳)

相關主題

商品描述

This book provides a ‘one-stop source’ for all readers who are interested in a new, empirical approach to machine learning that, unlike traditional methods, successfully addresses the demands of today’s data-driven world. After an introduction to the fundamentals, the book discusses in depth anomaly detection, data partitioning and clustering, as well as classification and predictors. It describes classifiers of zero and first order, and the new, highly efficient and transparent deep rule-based classifiers, particularly highlighting their applications to image processing. Local optimality and stability conditions for the methods presented are formally derived and stated, while the software is also provided as supplemental, open-source material. The book will greatly benefit postgraduate students, researchers and practitioners dealing with advanced data processing, applied mathematicians, software developers of agent-oriented systems, and developers of embedded and real-time systems. It can also be used as a textbook for postgraduate coursework; for this purpose, a standalone set of lecture notes and corresponding lab session notes are available on the same website as the code.

商品描述(中文翻譯)

本書為所有對於一種新的實證機器學習方法感興趣的讀者提供了一個「一站式資源」，這種方法與傳統方法不同，成功地滿足了當今數據驅動世界的需求。在介紹基本原理後，本書深入探討了異常檢測、數據劃分和聚類，以及分類和預測器。它描述了零階和一階的分類器，以及新的、高效且透明的深度基於規則的分類器，特別強調其在圖像處理中的應用。對於所提出的方法，本書正式推導並陳述了局部最優性和穩定性條件，同時也提供了作為補充的開源軟體。本書將對於從事高級數據處理的研究生、研究人員和實務工作者、應用數學家、面向代理系統的軟體開發者，以及嵌入式和即時系統的開發者大有裨益。它也可以作為研究生課程的教科書；為此，獨立的講義和相應的實驗課筆記可在與程式碼相同的網站上獲得。

作者簡介

Dimitar Filev, Henry Ford Technical Fellow, Ford Motor Company, USA, and Member of the National Academy of Engineering, USA: “The book Empirical Approach to Machine Learning opens new horizons to automated and efficient data processing.”

Paul J. Werbos, Inventor of the back-propagation method, USA: “I owe great thanks to Professor Plamen Angelov for making this important material available to the community just as I see great practical needs for it, in the new area of making real sense of high-speed data from the brain.” 
Chin-Teng Lin, Distinguished Professor at University of Technology Sydney, Australia: “This new book will set up a milestone for the modern intelligent systems.”
Edward Tunstel, President of IEEE Systems, Man, Cybernetics Society, USA: “Empirical Approach to Machine Learning provides an insightful and visionary boost of progress in the evolution of computational learning capabilities yielding interpretable and transparent implementations.”

作者簡介(中文翻譯)

Dimitar Filev，美國福特汽車公司技術研究員，及美國國家工程院院士：「這本《實證機器學習方法》為自動化和高效數據處理開啟了新的視野。」



Paul J. Werbos，反向傳播方法的發明者，美國：「我非常感謝Plamen Angelov教授，讓這些重要的材料能夠提供給社群，正如我所看到的，這在理解來自大腦的高速數據的新領域中有著巨大的實際需求。」

Chin-Teng Lin，澳大利亞悉尼科技大學的傑出教授：「這本新書將為現代智能系統樹立一個里程碑。」

Edward Tunstel，美國IEEE系統、人類、控制論學會會長：「《實證機器學習方法》提供了對計算學習能力演進的深刻和前瞻性的推進，產生可解釋和透明的實現。」

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