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(Machine Learning Foundations)—Mathematical Foundations

，副教授 (Associate Professor)，资讯工程学系 (Computer Science and Information Engineering)

What is Overfitting?

• bad generalization: low $E_{in}$, high $E_{out}$

• example

  

• Cause of Overfitting

  • excessive $d_{VC}$
  • noise
  • limited data size

The Role of Noise and Data Size

• concession for advantage

• Learning Curves Revisited

  

• ‘target complexity’ acts like noise

Deterministic Noise

• A Detailed Experiment

  

• The Results

  

  • impact of $σ^2$ versus N: stochastic noise
  • impact of $Q_f$ versus N: deterministic noise

• four reasons of serious overfitting

  

  overfitting ‘easily’ happens

• Deterministic Noise

  

  pseudo-random generator 伪随机数发生器

Dealing with Overfitting

Driving Analogy Revisited

• correct the label (data cleaning)
• remove the example (data pruning)
• add virtual examples by shifting/rotating the given digits (data hinting)

possibly helps, but effect varies (改变数据的分布)