Difference between revisions of "Class meeting for 10-405 Parallel Perceptrons"

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* [http://www.cs.cmu.edu/~wcohen/10-601/vp-notes/vp.pdf Notes on voted perceptron.]
 
* [http://www.cs.cmu.edu/~wcohen/10-601/vp-notes/vp.pdf Notes on voted perceptron.]
  
=== Readings  ===
+
=== Optional Readings  ===
 
* [https://cseweb.ucsd.edu/~yfreund/papers/LargeMarginsUsingPerceptron.pdf Large Margin Classification Using the Perceptron Algorithm], Freund and Schapire, MLJ 1999
 
* [https://cseweb.ucsd.edu/~yfreund/papers/LargeMarginsUsingPerceptron.pdf Large Margin Classification Using the Perceptron Algorithm], Freund and Schapire, MLJ 1999
 
* [http://www.cs.columbia.edu/~mcollins/papers/tagperc.pdf Discriminative Training Methods for Hidden Markov Models], Collins EMNLP 2002.
 
* [http://www.cs.columbia.edu/~mcollins/papers/tagperc.pdf Discriminative Training Methods for Hidden Markov Models], Collins EMNLP 2002.

Revision as of 11:28, 6 March 2018

This is one of the class meetings on the schedule for the course Machine Learning with Large Datasets 10-405 in Spring 2018.

Slides

Quiz

Readings

Optional Readings

Things to Remember

  • Definition of mistake bound
  • Definition of perceptron algorithm
    • Mistake bound analysis for perceptrons, in terms of margin and example radius
  • Converting perceptrons to batch: voted perceptron, averaged perceptron
  • Definition of the ranking perceptron and kernel perceptron
  • Relationship of hash trick to kernels
  • Parallellizing streaming ML algorithms
    • Parameter mixing, and the effect it has on the mistake bounds for perceptrons
    • Iterative parameter mixing, and the effect it has on the mistake bounds for perceptrons
  • The ALLREDUCE algorithm and its complexity