Class meeting for 10-605 Parallel Perceptrons 2
From Cohen Courses
This is one of the class meetings on the schedule for the course Machine Learning with Large Datasets 10-605 in Fall_2015.
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 give for convergence of the basic perceptron and the IPM version
