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Machine Learning for Language Technology Lecture 11: Logis.c Regression
Marina San.ni Department of Linguis.cs and Philology Uppsala University, Uppsala, Sweden
Autumn 2014
Acknowledgement: Thanks to Prof. Joakim Nivre for course design and materials
1
”Our Linear” Classifiers and their Induc.ve biases (or… how to find the weights)
• Perceptron (online): minimizes error in the training set
• SVMs (batch): minimizes error in the training set and maximizes margin
• MIRA (online): minimizes error in the training set and maximizes margin
• Logis.c Regression (batch): maximizes the likelihood of the training data
Appendix: Gradient Ascent
The end