Machine Learning Meetup Notes: 2010-04-28

From Noisebridge
Jump to: navigation, search
  • 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 (and their associated multipliers)
  • Thomas talked about the KDD conference and their data competition [4]
  • Sai skyped in and talked a bit about his use of libSVM for classification of user history on his website
  • We talked a little bit about libSVM [5]
  • Ted posted some links: