2-ML的分类

1. ML的基本分类：

1.1 不同的\(y\)类型：

• 二元分类：
  
• 多元分类：
  
• 回归：
  
• 结构学习：
  

1.2 是否有\(y\)：

• 有监督（supervised）:
  Coin Recognition：every \(x_n\) comes with corresponding \(y_n\)
  

• 无监督（unsupervised）:
  
  
  

• 半监督（semi-supervised）:
  Semi-supervised: Coin Recognition with Some yn
  
  

• 强化学习（reinforcement）:
  
  
  

1.3 不同的学习策略（用户和机器的交互方式）：

• 批处理（batch learning）:
  batch supervised multiclass classification: learn from all known data
  

• 在线学习（online learning）:
  尤其适用于结合流式计算，即数据是逐个产生，串行输入，无法batch
  
  【注】：

1. 联想PLA的最简单实现版本，就是online-learning：每次发现一个点犯错，就立刻修改\(w\)，知错就改
2. reinforcement learning is often done online ：因为需要逐步修正model
3. online hypothesis ‘improves’ through receiving data instances sequentially

• Active(主动学习)：
  improve hypothesis with fewer labels (hopefully) by asking questions strategically

1.4 不同的输入空间\(\mathcal{X}\)：

• concrete faeture:
  concrete features: each dimension of \(X ⊆ R^d\) represents 'sophisticated' physical meaning
  

• Raw Features:
  such as Digit Recognition Problem，raw input space like this,
  

• Other Problems with Raw Features: image pixels, speech signal, etc.
• raw features: often need human or machines to convert to concrete ones.

• Abstract Features:
  abstract: again need ‘feature conversion/extraction/construction’
  

2. 总结：