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 ModelingThis 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:Chinese Word Segmentation |