Machine Learning Meetup Notes: 2010-04-28

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  • Mike S presented a mathematical overview of SVMs
    • Started with introduction to linear classification [1]
    • Discussed the kernel trick [2]
    • Loosely derived the loss function and dual loss function for support vector machines [3]
    • Emphasized two important aspects of SVMs:
      • Dual problem is a quadratic programming problem that is easier to solve than the primal problem
      • After dual problem is optimized, only the support vectors (the data points whose langrangian multipliers are > 0) are needed to make predictions for new data
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