Pattern Recognition, 4/e (Hardcover)

Sergios Theodoridis, Konstantinos Koutroumbas

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<內容簡介>

This book considers classical and current theory and practice, of both supervised and unsupervised pattern recognition, to build a complete background for professionals and students of engineering. The authors, leading experts in the field of pattern recognition, have provided an up-to-date, self-contained volume encapsulating this wide spectrum of information. The very latest methods are incorporated in this edition: semi-supervised learning, combining clustering algorithms, and relevance feedback.This edition includes many more worked examples and diagrams (in two colour) to help give greater understanding of the methods and their application. Computer-based problems will be included with MATLAB code. An accompanying book contains extra worked examples and MATLAB code of all the examples used in this book.

<章節目錄>

1. INTRODUCTION
2. CLASSIFIERS BASED ON BAYES DECISION THEORY
3. LINEAR CLASSIFIERS
4. NONLINEAR CLASSIFIERS
5. FEATURE SELECTION
6. FEATURE GENERATION I: DATA TRANSFORMATION AND DIMENSIONALITY REDUCTION
7. FEATURE GENERATION II
8. TEMPLATE MATCHING
9. CONTEXT DEPENDENT CLASSIFICATION
10. SYSTEM EVALUATION
11. CLUSTERING: BASIC CONCEPTS
12. CLUSTERING ALGORITHMS: ALGORITHMS L SEQUENTIAL
13. CLUSTERING ALGORITHMS II: HIERARCHICAL
14. CLUSTERING ALGORITHS III: BASED ON FUNCTION OPTIMIZATION
15. CLUSTERING ALGORITHMS IV: CLUSTERING
16. CLUSTER VALIDITY


 


 

 

 

商品描述(中文翻譯)

這本書考慮了監督式和非監督式模式識別的經典和現代理論和實踐,為工程專業人員和學生提供了完整的背景。作者是模式識別領域的領先專家,他們提供了一個最新的、自成一體的卷,涵蓋了這個廣泛的信息範圍。本版中包含了最新的方法:半監督學習、結合聚類算法和相關反饋。本版還包含了更多的實例和圖表(兩種顏色),以幫助更好地理解方法及其應用。將包含基於計算機的問題和MATLAB代碼。附帶的書籍包含了本書中使用的所有實例的額外實例和MATLAB代碼。

章節目錄:
1. 簡介
2. 基於貝葉斯決策理論的分類器
3. 線性分類器
4. 非線性分類器
5. 特徵選擇
6. 特徵生成I:數據轉換和維度降低
7. 特徵生成II
8. 模板匹配
9. 上下文相依分類
10. 系統評估
11. 聚類:基本概念
12. 聚類算法:順序算法
13. 聚類算法II:階層算法
14. 聚類算法III:基於函數優化
15. 聚類算法IV:聚類
16. 聚類有效性