Machine Learning Meetup Notes: 2010-04-28

• 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]
  • Started a wiki page for the competition: https://www.noisebridge.net/wiki/KDD_Competition_2010
• Sai skyped in and talked a bit about his use of libSVM for classification of user history on his website cssfingerprint.com
• We talked a little bit about libSVM [5]
• Ted posted some links:
  • WEKA, a variety pack of ML tools written in Java: http://www.cs.waikato.ac.nz/ml/weka/
  • KDD Datasets: http://kdd.ics.uci.edu/summary.data.type.html