Markov Models for Pattern Recognition: From Theory to Applications (Advances in Computer Vision and Pattern Recognition)
暫譯: 馬可夫模型於模式識別:從理論到應用(計算機視覺與模式識別進展)

Gernot A. Fink

  • 出版商: Springer
  • 出版日期: 2014-01-28
  • 售價: $3,830
  • 貴賓價: 9.5$3,639
  • 語言: 英文
  • 頁數: 276
  • 裝訂: Hardcover
  • ISBN: 1447163079
  • ISBN-13: 9781447163077
  • 相關分類: Computer Vision
  • 海外代購書籍(需單獨結帳)

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商品描述

This thoroughly revised and expanded new edition now includes a more detailed treatment of the EM algorithm, a description of an efficient approximate Viterbi-training procedure, a theoretical derivation of the perplexity measure and coverage of multi-pass decoding based on n-best search. Supporting the discussion of the theoretical foundations of Markov modeling, special emphasis is also placed on practical algorithmic solutions. Features: introduces the formal framework for Markov models; covers the robust handling of probability quantities; presents methods for the configuration of hidden Markov models for specific application areas; describes important methods for efficient processing of Markov models, and the adaptation of the models to different tasks; examines algorithms for searching within the complex solution spaces that result from the joint application of Markov chain and hidden Markov models; reviews key applications of Markov models.

商品描述(中文翻譯)

這本經過徹底修訂和擴充的新版本現在包含了對 EM 演算法的更詳細處理,描述了一種高效的近似 Viterbi 訓練程序,對困惑度(perplexity)度量的理論推導,以及基於 n-最佳搜尋的多次解碼的覆蓋。支持馬可夫建模理論基礎的討論,特別強調實際的演算法解決方案。特點包括:介紹馬可夫模型的正式框架;涵蓋概率量的穩健處理;提出針對特定應用領域的隱馬可夫模型配置方法;描述高效處理馬可夫模型的重要方法,以及將模型適應於不同任務的過程;檢視在馬可夫鏈和隱馬可夫模型聯合應用所產生的複雜解空間中進行搜尋的演算法;回顧馬可夫模型的關鍵應用。