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adaboost

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资源说明：Adaboost implementation using bayesian and decision tree classifiers

Adaboost
==========

We implement two different types of classifier:
- Naive Bayesian Classifier (NBC)
- Decision Tree Classifier (DTC)

Usage
---------

 - f      The name of the data file
 - m           Max number of different values per feature
 - n       Number of NBC and DTC
 - p    The percentage of the data set to be used for training
 - d         The maximum depth for the DTCs [Default: 0 which means maximum possible depth]
 - k                Keep losers

If you want to work with the page-blocks dataset, use 10 NBC and 10 DTC(with a max depth of 2) and split the use 80% of the dataset as a training set and the other 20% as a test set, type de following parameters:
    
    -f datasets/page-blocks.txt -m 50 -n 10 0 -p 80 -d 2

Good choice for parameter m
-----------------
For dataset:

 - "page-blocks" use m = 75
 - "glass" use m = 15
 - "pen-digits" use m = 100
 - "yeast" use m = 100
 - "optdigits" use m = 16

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