Machine Learning Meetup Notes: 2010-04-28 - Revision history

Mschachter at 16:18, 29 April 2010

2010-04-29T16:18:21Z

← Older revision		Revision as of 09:18, 29 April 2010
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	*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

Mschachter at 16:16, 29 April 2010

2010-04-29T16:16:26Z

← Older revision		Revision as of 09:16, 29 April 2010
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	***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/]

Mschachter at 16:13, 29 April 2010

2010-04-29T16:13:51Z

← Older revision		Revision as of 09:13, 29 April 2010
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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]
	]
	*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/]

Mschachter at 16:13, 29 April 2010

2010-04-29T16:13:40Z

← Older revision		Revision as of 09:13, 29 April 2010
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	**Emphasized two important aspects of SVMs:		**Emphasized two important aspects of SVMs:
	***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		***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/]

Mschachter: Created page with '*Mike S presented a mathematical overview of SVMs Started with introduction to linear classification [http://en.wikipedia.org/wiki/Linear_classifier] Discussed the kernel tri…'

2010-04-29T16:10:31Z

Created page with '*Mike S presented a mathematical overview of SVMs **Started with introduction to linear classification [http://en.wikipedia.org/wiki/Linear_classifier] **Discussed the kernel tri…'

New page

*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

Machine Learning Meetup Notes: 2010-04-28 - Revision history

Mschachter at 16:18, 29 April 2010

Mschachter at 16:16, 29 April 2010

Mschachter at 16:13, 29 April 2010

Mschachter at 16:13, 29 April 2010

Mschachter: Created page with '*Mike S presented a mathematical overview of SVMs **Started with introduction to linear classification [http://en.wikipedia.org/wiki/Linear_classifier] **Discussed the kernel tri…'

Mschachter: Created page with '*Mike S presented a mathematical overview of SVMs Started with introduction to linear classification [http://en.wikipedia.org/wiki/Linear_classifier] Discussed the kernel tri…'