Difference between revisions of "Class meeting for 10-605 Parallel Perceptrons 2"
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| − | This is one of the class meetings on the [[Syllabus for Machine Learning with Large Datasets 10-605 in Fall | + | This is one of the class meetings on the [[Syllabus for Machine Learning with Large Datasets 10-605 in Fall 2016|schedule]] for the course [[Machine Learning with Large Datasets 10-605 in Fall_2016]]. |
=== Slides === | === Slides === | ||
Revision as of 13:56, 8 August 2016
This is one of the class meetings on the schedule for the course Machine Learning with Large Datasets 10-605 in Fall_2016.
Slides
Perceptrons, continued:
Parallel perceptrons with iterative parameter mixing:
Readings for the Class
- Distributed Training Strategies for the Structured Perceptron, R. McDonald, K. Hall and G. Mann, North American Association for Computational Linguistics (NAACL), 2010.
Optional Readings
What you should remember
- The averaged perceptron and the voted perceptron
- Approaches to parallelizing perceptrons (and other on-line learning methods, like SGD)
- Parameter mixing
- Iterative parameter mixing (IPM)
- The meaning and implications of the theorems given for convergence of the basic perceptron and the IPM version
