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贝叶斯学习算法分类文本。基于朴素贝叶斯分类器的文本分类的通用算法

Bag Of Words Library README
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`libbow', version 0.8.

   `Libbow' is a library of C code intended for writing statistical
text-processing programs.  This distribution includes the library, as
well as a text classification front-end, and a document retrieval
front-end.

The library provides facilities for:
        Recursively descending directories, finding text files.
        Finding `document' boundaries when there are multiple docs per file.
        Tokenizing a text file, according to several different methods.
        Including N-grams among the tokens.
        Mapping strings to integers and back again, very efficiently.
        Building a sparse matrix of document/token counts.
        Pruning vocabulary by occurrence counts or by information gain.
        Building and manipulating word vectors.
        Setting word vector weights according to NaiveBayes, TFIDF, and a
          simple form of Probabilistic Indexing.
        Scoring queries for retrieval or classification.
        Writing all data structures to disk in a machine-architecture-
          independent format.
        Reading the document/token matrix from disk in an efficient,
          sparse fashion.
        Performing test/train splits, and automatic classification tests.

   It should compile on most UNIX systems, and WindowsNT (with a GNU
build environment).

   The code conforms to the GNU coding standards.  It is released under
the Library GNU Public License.

The library does not:
        Have parsing facilities.
        Do smoothing across N-gram models.
        Claim to be finished.
        Have good documentation.
        Claim to be bug-free.
        ...many other things.

Rainbow
=======

   `Rainbow' is a standalone program that does document classification.
Here are some examples:

   *      rainbow -i ./training/positive ./training/negative

     Using the text files found under the directories `./positive' and
     `./negative', tokenize, build word vectors, and write the
     resulting data structures to disk.

   *      rainbow -q ./testing/254

     Tokenize the text document `./testing/254', and classify it,
     producing output like:

          /home/mccallum/training/positive 0.72
          /home/mccallum/training/negative 0.28

   *      rainbow -t 5

     Perform 5 trials, each consisting of a test/train split, a
     resetting of weights according to the new split, and outputs of
     the classification of the test documents.

   Typing `rainbow --help' will give list of all rainbow options.

   After you have compiled `libbow' and `rainbow', you can run the
shell script `./demo/script' to see an annotated demonstration of the
classifier in action.

   The web page
http://www.cs.cmu.edu/afs/cs.cmu.edu/project/theo-11/www/naive-bayes.html
has a pointer to a "Gentle Introduction to Rainbow", as well as some
sample UseNet text data.

Rainbow improvements coming soon:
   Better documentation.
   Better modularily of command-line options for changing parameters
     of weight-setting methods.
   Incremental model training.
   Better smoothing.  Good-Turing estimates, etc.

Arrow
=====

   `Arrow' is a standalone program that does document retrieval.
Sorry, there is no documentation yet.