Data Clustering and Pattern Recognition (資料分群與樣式辨認)

Roger Jang (張智星)


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Table of Contents

Chapter 1: Introduction

1-1:About This Book (有關本書)
1-2:Example Programs (如何取得程式碼)
1-3:Web Resources (網路資源)

Chapter 2: Commonly Used Datasets (常用資料集)

[Slides]
2-1:Intro. to Datasets
2-2:Iris Dataset
2-3:Wine Dataset
2-4:Abalone Dataset

Chapter 3: Data Clustering (資料群聚)

3-1:Introduction (簡介)
3-2:Hierarchical Clustering (階層式分群法)
3-3:K-means Clustering
3-4:模糊C-means分群法
3-5:向量量化
Chapter 3: Exercises

Chapter 4: MLE for PDF Modeling

This chapter introduces the basics of dynamic programming, including its principle and applications.
[Video][Slides]
4-1:Intro. to MLE
4-2:MLE for discrete event
4-3:PDF Modeling for 1D Gaussian
4-4:PDF Modeling for ND Gaussian
Chapter 4: Exercises

Chapter 5: Pattern Recognition (樣式辨認)

5-1:Intro. to PR
5-2:K-nearest-neighbor Classifiers
5-3:Learning Vector Quantization (學習式向量量化)
5-4:Linear Classifiers (線性分類器)
5-5:Naive Bayes Classifiers (單純貝氏分類器)
5-6:Quadratic Classifiers (二次分類器)
5-7:貝式分類器
Chapter 5: Exercises

Chapter 6: Performance Evaluation of Classifiers (分類器效能評估)

6-1:Intro. to Recognition Rate Estimate of Classifiers (簡介)
6-2:Methods for Recognition Rate Estimate (辨識率預估)
Chapter 6: Exercises

Chapter 7: GMM

7-1:GMM Introduction
7-2:GMM Application: PDF Modeling
7-3:GMM Application: Classification
Chapter 7: Exercises

Chapter 8: Dynamic Programming (動態規劃)

This chapter introduces the basics of dynamic programming, including its principle and applications.
[Video][Slides]
8-1:Introduction to Dynamic Programming (動態規劃)
8-2:Longest Common Subsequence
8-3:Edit Distance
8-4:Dynamic Time Warping
8-5:DTW for Speaker Identification
8-6:DTW for Speaker Identification: Further Enhancement
Chapter 8: Exercises

Chapter 9: Hidden Markov Models (HMM)

9-1:Introduction (簡介)
9-2:Discrete HMM
9-3:Continuous HMM
Chapter 9: Exercises

Chapter 10: Feature Selection (特徵選取)

10-1:Introduction (簡介)
10-2:Feature Selection Methods (特徵選取方法)
Chapter 10: Exercises

Chapter 11: Feature Extraction (特徵粹取)

11-1:Introduction (簡介)
11-2:PCA (主要分量分析)
11-3:LDA (線性識別分析)
11-4:PCA for Face Recognition
11-5:LDA for Face Recognition
Chapter 11: Exercises

Chapter 12: 用於分類的資料量縮減

12-1:簡介
12-2:資料編修
12-3:資料濃縮

Chapter 13: Application Case Study (應用案例說明)

13-1:Leaf Recognition
13-2:Human Identification
13-3:Finger-Ready Detection
13-4:Emoticon Mood Recognition