42 lectures + 3 for exams

### 模式识别

• preprocessing
• video, img, signal processing
• pattern separation
• location detection / background / pattern-pattern separation
• motion analysis
• object tracking / movement pattern
• pattern desp and classification
• feature extraction/selection, classification, clustering, ML
• application (on specific system)

### topics in pattern classification

… blabla …

multi-modal

semi-supervised class.. / clustering distance metric learning weak supervised ???

### pattern recognition formalization

example: human face recognition problem

• 2-class / multi-class
• classifier design: ML
• related: feature extraction / selection

• 类别模型: $M_i=M(x, \theta_i)$
• 判别函数: $y_i=f(x, w_i)$ (discriminant function) 有的没有判别函数 本门课程大多可以写出

• distance metric(similarity): $\min_i d(x, M_i)$
• decision region: $R_i=\arg\max_i f(x,w_i)$

#### fish example

firstly, data collection and fish feature distribution analsis

small dataset -> non-linear huge dataset(network / graph) -> linear (due to the huge amount and very high dimension, linear is good enough)

model complexity: string -> curve -> k-nearest-neighbor

#### systematic flow

data acquisition -> layout analysis -> character segmentation -> recognition -> post-preprocessing

?? recall: use multi-vector rather than a integer (compact embedding) ( [1,0,0]..[0,0,1] over 1, 2, 3 )

### method category

statistical leanring / structual

statistical: parameterized / non-parameterized / … / ensemble / … structual: graph / …

other classification

??? blaaaaaaa

by generative / discriminative

generative: how to generative 找到内控因素，每个类分别学习，parameters are continuous

• $p(\bf{x} \vert C) = f(\bf{x}, \theta)$ parameterized model
• bayesian direct graph
• (template-based classifier ???)
• HMM

discriminative: given sample and label, unaware about how it is generated，所有类同时学习

• Artificial NN
• SVM
• Boosting
• CRF (in Ph.D. course)