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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)

Linear Models for Binary Classification

• $ys$: classification correctness score 分类正确性分数

• Visualizing Error Functions

  

• $small\ E^{CE}_{in} (\mathbf w) \Rightarrow small\ E_{out}^{0/1}(\mathbf w)$

  logistic/linear reg. for linear classification

• linear regression sometimes used to set $\mathbf w_0$ for PLA/pocket/logistic regression

• logistic regression often preferred over pocket

Stochastic Gradient Descent

• stochastic gradient = true gradient + zero-mean ‘noise’ directions
  
• two practical rule-of-thumb:
  • stopping condition? t large enough
  • η η <script type="math/tex" id="MathJax-Element-13">η</script>? 0.1 when x in proper range

Multiclass via Logistic Regression

• One Class at a Time Softly 属于某个类别的概率

• One-Versus-All (OVA) Decomposition

  

Multiclass via Binary Classification

• One-versus-one (OVO) Decomposition

  

  voting of classifiers