Bayesian classifiers in ruby?

I’m researching existing Bayesian classifiers in Ruby – it looks as
if there are two, one called Bishop, a Python port, and another called
Classifier.

Has anybody worked with them? Any upsides, downsides? Both theoretical
and practical perspectives. Partly to expand my brain and partly for
the sake of putting some real software together.

Giles B. a écrit :

I’m researching existing Bayesian classifiers in Ruby – it looks as
if there are two, one called Bishop, a Python port, and another called
Classifier.

Has anybody worked with them? Any upsides, downsides? Both theoretical
and practical perspectives. Partly to expand my brain and partly for
the sake of putting some real software together.

I have used Bishop to classify the 488 articles of the Project of
European Constitution and it was helpful.
Bishop is very simple and consists of just one file and a couple of
classes. There is room for improvement in the way it tokenizes code
source.
Classifier is more complex, with multiple files and more classes.
Morever it may use the Gnu Scientific Library to perform its
calculation,
I have recently tried to use both of them to help a teacher classify
CS homeworks and analyze how many different solutions the students
had come up with. I used simple example like printing number from 0 to
10 in C. In one case, I used a “for” loop, and in another I used a
“while” loop. Then I tested a candidate program using a while loop with
different variable naems. Bishop’s guess was that it was more like the
‘for’ loop. Not very conclusive.
Classifier did not better in that it failed to tokenize the C source
text. It was trying to stem a keyword but failed.

As long as you analyze natural language, both seem suited, although with
different degrees of complexity under the hood, both have a very simple
interface: define a category and train it. Then a guess interface to
evaluate candidates.

J-P

I wrote a bayesian classifier to classify nursing home calls. If you
want I can email you the source. It works with about a 95% accuracy.
Tom R.

Definitely! That would be very cool.

As long as you analyze natural language, both seem suited, although with
different degrees of complexity under the hood, both have a very simple
interface: define a category and train it. Then a guess interface to
evaluate candidates.

I’m hoping to develop yet another spam filter. in that sense I can
only say I’m sort of analyzing natural language. Not all of it is
natural language, some of it is code. In the Paul Graham thing where
he came up with this idea, if I remember right, he said that a font
tag with the color red turned out to be the single most reliable
indicator of spam. Obviously in HTML e-mail there are going to be
similar trends. However if the tokenizer is the only problem that may
be something I can change without too much stress.

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In article
[email protected],
Giles B. [email protected] wrote:

indicator of spam. Obviously in HTML e-mail there are going to be
similar trends. However if the tokenizer is the only problem that may
be something I can change without too much stress.

Long ago, I wrote an interface to the ifile program and I use
that in my spam/email filtering. ifile is abandomware at the
moment. I think I posted it on the ruby mailing list at ome
point, you might try searching for it.

_ Booker C. Bense

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