Kernel Methods and Machine Learning
暫譯: 核方法與機器學習

S. Y. Kung

  • 出版商: Cambridge
  • 出版日期: 2014-06-23
  • 售價: $3,160
  • 貴賓價: 9.5$3,002
  • 語言: 英文
  • 頁數: 572
  • 裝訂: Hardcover
  • ISBN: 110702496X
  • ISBN-13: 9781107024960
  • 相關分類: Machine Learning
  • 立即出貨 (庫存 < 3)

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商品描述

Offering a fundamental basis in kernel-based learning theory, this book covers both statistical and algebraic principles. It provides over 30 major theorems for kernel-based supervised and unsupervised learning models. The first of the theorems establishes a condition, arguably necessary and sufficient, for the kernelization of learning models. In addition, several other theorems are devoted to proving mathematical equivalence between seemingly unrelated models. With over 25 closed-form and iterative algorithms, the book provides a step-by-step guide to algorithmic procedures and analysing which factors to consider in tackling a given problem, enabling readers to improve specifically designed learning algorithms, build models for new applications and develop efficient techniques suitable for green machine learning technologies. Numerous real-world examples and over 200 problems, several of which are Matlab-based simulation exercises, make this an essential resource for graduate students and professionals in computer science, electrical and biomedical engineering. Solutions to problems are provided online for instructors.

商品描述(中文翻譯)

本書提供了基於核心學習理論的基本基礎,涵蓋了統計和代數原則。它提供了超過30個主要定理,針對基於核心的監督學習和非監督學習模型。第一個定理建立了一個條件,這個條件可以說是核心化學習模型的必要且充分條件。此外,還有幾個定理專門用於證明看似無關的模型之間的數學等價性。書中提供了超過25個封閉形式和迭代算法,並提供逐步指導算法程序以及分析在解決特定問題時需要考慮的因素,使讀者能夠改進專門設計的學習算法,為新應用構建模型,並開發適合綠色機器學習技術的高效技術。書中包含大量的實際案例和超過200個問題,其中幾個是基於Matlab的模擬練習,這使得本書成為計算機科學、電氣工程和生物醫學工程研究生及專業人士的重要資源。問題的解答在線上提供給教師使用。