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- The best reference for machine learning (alas it is very expensive, so
you might wish to go to the library or ask me to xerox pages for you):
Machine Learning, T. Mitchell, McGraw-Hill, 1997.
- A draft chapter on Decision Tree
Induction by Nils Nilsson.
- The SPRINT
paper, which explains how to scale a decision tree learner to handle data
which is much longer than memory.
- Naïve Bayes and
Nearest Neighbor by Estelle Brand and Rob Gerritsen
- Chumki Basu, Haym Hirsh, and William W. Cohen (1998).
Recommendation as Classification:
Using Social and Content-Based Information in Recommendation.
Proceedings of the Fifteenth National Conference on
Artificial Intelligence (AAAI98).
AAAI Press/MIT Press.
- The original paper describing RIPPER, a fast
rule learner which has proven to be a good tool for learning from
unstructured text.
- Two approaches to organizing web search results:
- Visualizing weblogs by learning markov models short and medium
length written versions.
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