Difference between revisions of "Class meeting for 10-605 in Fall 2016 Streaming Naive Bayes"
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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 15:25, 1 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
- Slides in Powerpoint, in PDF - the stream-and-sort pattern, and large-vocabulary Naive Bayes
Readings for the Class
- Required: my notes on streaming and Naive Bayes
- Optional: If you're interested in reading more about smoothing for naive Bayes, I recommend this paper: Peng, Fuchun, Dale Schuurmans, and Shaojun Wang. "Augmenting naive Bayes classifiers with statistical language models." Information Retrieval 7.3 (2004): 317-345.
Things to Remember
- Zipf's law and the prevalence of rare features/words
- Communication complexity
- Stream and sort
- Complexity of merge sort
- How pipes implement parallel processing
- How buffering output before a sort can improve performance
- How stream-and-sort can implement event-counting for naive Bayes
