# Machine Learning Meetup Notes: 2010-04-28

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***Dual problem is a quadratic programming problem that is easier to solve than the primal problem | ***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) | ***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 | + | *Thomas talked about the KDD conference and their data competition [https://pslcdatashop.web.cmu.edu/KDDCup/rules_data_format.jsp] |

− | ] | + | **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 | *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/] | *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 |

## Latest revision as of 09:18, 29 April 2010

- 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