Difference between revisions of "Class meeting for 10-605 Workflows For Hadoop"
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=== Slides === | === Slides === | ||
| − | * | + | * Slides [http://www.cs.cmu.edu/~wcohen/10-605/workflow-1.pptx in Powerpoint], [http://www.cs.cmu.edu/~wcohen/10-605/workflow-1.pdf in PDF], |
| + | === Quiz === | ||
| + | |||
| + | * [https://qna-app.appspot.com/edit_new.html#/pages/view/aglzfnFuYS1hcHByGQsSDFF1ZXN0aW9uTGlzdBiAgICwmqv_Cww | ||
| + | Todays quiz] | ||
=== Readings === | === Readings === | ||
Revision as of 17:28, 12 September 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,
Quiz
Todays quiz]
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.
Things to Remember
- The TFIDF representation for documents.
- The Rocchio algorithm.
- Why Rocchio is easy to parallelize.
- Definition of a similarity join/soft join.
- Why inverted indices make TFIDF representations useful for similarity joins
- e.g., whether high-IDF words have shorter or longer indices, and more or less impact in a similarity measure
