Difference between revisions of "Class meeting for 10-605 Rocchio and Hadoop Workflows"
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=== Slides === | === Slides === | ||
| + | |||
| + | Workflows for Hadoop: | ||
| + | |||
| + | * [http://www.cs.cmu.edu/~wcohen/10-605/beyond-hadoop.pptx Workflows for Hadoop] | ||
| + | * The phrases example: | ||
| + | ** [http://www.cs.cmu.edu/~wcohen/10-605/pig-example/phrases.pig PIG source code] | ||
| + | ** [http://www.cs.cmu.edu/~wcohen/10-605/pig-example/SmoothedPKL.java Java source code] | ||
| + | * Some other examples: | ||
| + | ** [http://www.cs.cmu.edu/~wcohen/10-605/pig-example/phirl-naive.pig Naive Similarity Join] | ||
| + | ** [http://www.cs.cmu.edu/~wcohen/10-605/pig-example/phirl.pig Optimized Similarity Join] | ||
| + | |||
| + | Rocchio: | ||
* [http://www.cs.cmu.edu/~wcohen/10-605/rocchio.pptx Rocchio - Another Fast Streaming Learning Algorithm - PPT], [http://www.cs.cmu.edu/~wcohen/10-605/rocchio.pdf PDF] | * [http://www.cs.cmu.edu/~wcohen/10-605/rocchio.pptx Rocchio - Another Fast Streaming Learning Algorithm - PPT], [http://www.cs.cmu.edu/~wcohen/10-605/rocchio.pdf PDF] | ||
| + | Also: | ||
| + | * [http://www.cs.cmu.edu/~wcohen/10-605/pig-example/tips-for-debugging-pig.txt My comments on debugging PIG.] | ||
| + | |||
| + | === Readings === | ||
| + | |||
| + | * Pig: none required. A nice on-line resource for PIG is the on-line version of the O'Reilly Book [http://chimera.labs.oreilly.com/books/1234000001811/index.html Programming Pig]. | ||
=== Readings for the Class === | === Readings for the Class === | ||
Revision as of 14:17, 17 September 2015
This is one of the class meetings on the schedule for the course Machine Learning with Large Datasets 10-605 in Fall_2015.
Slides
Workflows for Hadoop:
- Workflows for Hadoop
- The phrases example:
- Some other examples:
Rocchio:
Also:
Readings
- Pig: none required. A nice on-line resource for PIG is the on-line version of the O'Reilly Book Programming Pig.
Readings for the Class
- Introduction to Information Retrieval, by Christopher D. Manning, Prabhakar Raghavan & Hinrich Schütz, has a fairly self-contained chapter on the vector space model, including Rocchio's method.
Also discussed
- Joachims, Thorsten, A Probabilistic Analysis of the Rocchio Algorithm with TFIDF for Text Categorization. Proceedings of International Conference on Machine Learning (ICML), 1997.
- Relevance Feedback in Information Retrieval, SMART Retrieval System Experiments in Automatic Document Processing, 1971, Prentice Hall Inc.
- Schapire et al, Boosting and Rocchio applied to text filtering, SIGIR 98.
- Littlestone, Learning quickly when irrelevant attributes abound: A new linear-threshold algorithm, MLJ 1988. Includes the mistake-bound theory.
