Kernel Methods and Machine Learning

S. Y. Kung

  • 出版商: Cambridge
  • 出版日期: 2014-04-17
  • 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礎知識,涵蓋了統計和代數原理。它提供了30多個關於基於核心的監督和非監督學習模型的主要定理。其中第一個定理建立了一個條件,可以說是核心化學習模型的必要且充分條件。此外,還有幾個定理用於證明看似無關的模型之間的數學等價性。本書提供了25多個閉式和迭代算法,逐步指導讀者進行算法程序和分析,以解決特定問題,從而改進設計的學習算法,構建新應用的模型,並開發適用於綠色機器學習技術的高效技術。大量的實際例子和200多個問題,其中幾個是基於Matlab的模擬練習,使本書成為計算機科學、電氣工程和生物醫學工程的研究生和專業人士的必備資源。問題的解答可以在線上獲得,供教師使用。