Digital Signal Processing with Kernel Methods (Hardcover)

A realistic and comprehensive review of joint approaches to machine learning and signal processing algorithms, with application to communications, multimedia, and biomedical engineering systems

Digital Signal Processing with Kernel Methods reviews the milestones in the mixing of classical digital signal processing models and advanced kernel machines statistical learning tools. It explains the fundamental concepts from both fields of machine learning and signal processing so that readers can quickly get up to speed in order to begin developing the concepts and application software in their own research.

Digital Signal Processing with Kernel Methods provides a comprehensive overview of kernel methods in signal processing, without restriction to any application field. It also offers example applications and detailed benchmarking experiments with real and synthetic datasets throughout. Readers can find further worked examples with Matlab source code on a website developed by the authors. 

• Presents the necessary basic ideas from both digital signal processing and machine learning concepts
• Reviews the state-of-the-art in SVM algorithms for classification and detection problems in the context of signal processing
• Surveys advances in kernel signal processing beyond SVM algorithms to present other highly relevant kernel methods for digital signal processing

An excellent book for signal processing researchers and practitioners, Digital Signal Processing with Kernel Methods will also appeal to those involved in machine learning and pattern recognition. 

《具體應用於通訊、多媒體和生物醫學工程系統的機器學習和信號處理算法聯合方法的實際和全面評論》是一本對數字信號處理模型和高級核心機器統計學習工具的結合進行了里程碑式回顧的書籍。該書解釋了機器學習和信號處理兩個領域的基本概念，讓讀者能夠快速掌握這些概念，並開始在自己的研究中開發相應的概念和應用軟件。

《具體應用於通訊、多媒體和生物醫學工程系統的機器學習和信號處理算法聯合方法的實際和全面評論》提供了信號處理中核心方法的全面概述，不限於任何應用領域。書中還提供了實例應用和詳細的真實和合成數據集基準實驗。讀者可以在作者開發的網站上找到更多帶有Matlab源代碼的實例。

該書具有以下特點：
- 提供了數字信號處理和機器學習概念的基本思想
- 在信號處理背景下，回顧了支持向量機算法在分類和檢測問題中的最新進展
- 概述了除支持向量機算法之外的核心信號處理進展，介紹了其他相關的核心方法
- 非常適合信號處理研究人員和從業人員閱讀，同時也適合從事機器學習和模式識別的人士閱讀。