Editing Machine Learning Meetup Notes: 2010-04-28

*Mike S presented a mathematical overview of SVMs
**Started with introduction to linear classification [http://en.wikipedia.org/wiki/Linear_classifier]
**Discussed the kernel trick [http://en.wikipedia.org/wiki/Kernel_trick]
**Loosely derived the loss function and dual loss function for support vector machines [http://en.wikipedia.org/wiki/Support_vector_machine]
**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 [https://pslcdatashop.web.cmu.edu/KDDCup/rules_data_format.jsp]
*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 [http://www.csie.ntu.edu.tw/~cjlin/libsvm/]
*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