Multidimensional Particle Swarm Optimization for Machine Learning and Pattern Recognition (Adaptation, Learning, and Optimization)

Serkan Kiranyaz

  • 出版商: Springer
  • 出版日期: 2015-08-08
  • 售價: $4,410
  • 貴賓價: 9.5$4,190
  • 語言: 英文
  • 頁數: 352
  • 裝訂: Paperback
  • ISBN: 3642437621
  • ISBN-13: 9783642437625
  • 相關分類: ARMMachine Learning
  • 海外代購書籍(需單獨結帳)

買這商品的人也買了...

商品描述

For many engineering problems we require optimization processes with dynamic adaptation as we aim to establish the dimension of the search space where the optimum solution resides and develop robust techniques to avoid the local optima usually associated with multimodal problems. This book explores multidimensional particle swarm optimization, a technique developed by the authors that addresses these requirements in a well-defined algorithmic approach.

 

After an introduction to the key optimization techniques, the authors introduce their unified framework and demonstrate its advantages in challenging application domains, focusing on the state of the art of multidimensional extensions such as global convergence in particle swarm optimization, dynamic data clustering, evolutionary neural networks, biomedical applications and personalized ECG classification, content-based image classification and retrieval, and evolutionary feature synthesis. The content is characterized by strong practical considerations, and the book is supported with fully documented source code for all applications presented, as well as many sample datasets.

 

The book will be of benefit to researchers and practitioners working in the areas of machine intelligence, signal processing, pattern recognition, and data mining, or using principles from these areas in their application domains. It may also be used as a reference text for graduate courses on swarm optimization, data clustering and classification, content-based multimedia search, and biomedical signal processing applications.

商品描述(中文翻譯)

對於許多工程問題,我們需要具有動態適應性的優化過程,因為我們的目標是確定最佳解所在的搜索空間的維度,並開發強健的技術來避免通常與多模態問題相關的局部最優解。本書探討了多維粒子群優化,這是作者們開發的一種技術,以明確的算法方法滿足這些要求。

在介紹關鍵優化技術之後,作者們介紹了他們的統一框架,並展示了它在具有挑戰性的應用領域中的優勢,重點關注多維擴展的最新進展,如粒子群優化中的全局收斂、動態數據聚類、進化神經網絡、生物醫學應用和個性化心電圖分類、基於內容的圖像分類和檢索,以及進化特徵合成。內容具有強烈的實用考慮,並且本書提供了所有應用程序的完整文檔化源代碼,以及許多樣本數據集。

本書將對從事機器智能、信號處理、模式識別和數據挖掘領域的研究人員和從業人員,或在其應用領域中使用這些領域原則的人士有所裨益。它也可以作為群體優化、數據聚類和分類、基於內容的多媒體搜索以及生物醫學信號處理應用的研究生課程的參考書。