Major change to the website front page - feedback please

With all the questions today about what SpamBayes will work on, I think it
would be a good idea to make it easier for people to decide what to download
for different operating system/mail program combinations.  I've attached a
tentative replacement.  I would like feedback on the table right at the top
of the page.  For combinations which haven't been tested (e.g., IncrediMail,
Yahoo! Mail, etc), should I offer any download options?  Maybe just a link
to the 1.1a4 source?

At any rate, feedback please.  I'd like to install a replacement tomorrow if
possible to try and head off many of the types of questions we received
today.

Thanks,

Skip

http://www.student.virginia.edu/~improv/games/findthespam.html

SF Project Page

Frequently Asked Questions

About the Project

Home page

Background

Documentation

Applications

Reviews/Quotes

Developers

Related

Donate

Platforms

Windows

Unix/Linux

Mac OS

Getting the code

Releases

Subversion access

Contact Us

Contact details

http://sourceforge.net/projects/spambayes

SpamBayes Downloads

Operating System Mail Program Stable Release Test Release Notes

Windows XP Outlook 2000/2003/2007 1.0.4 1.1a4

Windows Vista Outlook 2003/2007 1.0.4 1.1a4

Windows XP Outlook Express 1.0.4 1.1a4

Windows Vista Windows Live Mail 1.0.4 1.1a4

Windows XP/Vista IncrediMail 1.1a4 Untested. POP3 only?

Any Thunderbird Thunderbayes Tighter integration similar to Outlook plugin

Any Gmail 1.1a4 POP3 works. IMAP maybe (untested).

Any Yahoo! Mail 1.1a4 Completely untested.

Any MSN Hotmail 1.1a4 Completely untested.

Any AOL Mail Won't work (closed system).

Mac OS X/Linux/Solaris Any 1.1a4 POP3/IMAP, etc. Use The Source Luke!

News

SpamBayes 1.0.4 is now available! (This includes both the source
archives and a Windows binary installer).

See the download page for more.

SpamBayes 1.1a4 is also now available! (This currently includes only the
source archives). This is an alpha
release, so you should only try it if you are willing to try out
experimental releases - otherwise stick with 1.0.4.

You may also like to see what other people have
been saying about us in the press and elsewhere .

What is SpamBayes?

The SpamBayes
project is working on developing a statistical (commonly, although a little
inaccurately, referred to as Bayesian )
anti-spam filter, initially based on the work of
Paul Graham.

The major difference between this and other, similar projects
is the emphasis on testing newer approaches to scoring messages.

While most anti-spam projects are still working with the
original graham algorithm, we found that a number of alternate methods
yielded a more useful response.

This is documented on the background page.

SpamBayes is not a single application. The core code is a message
classifier, however there are several applications available as part of the
SpamBayes project which use the classifier in specific contexts. For the
most part, the current crop of applications all operate on the client side
of things, however, a number of people have experimented with using
SpamBayes on mail servers to classify incoming mail for multiple users. The
table below outlines the main applications which are part of the SpamBayes
distribution.

Application
Description

Outlook Plugin
A plugin for Microsoft Outlook which tightly integrates classification
and training into the Outlook interface

Pop3proxy / sb_server
A mail filter which sits between the user's POP3 server(s) and the
user's mail client and presents a web-based training interface

Imapfilter
A mail filter similar to pop3proxy but which talks the IMAP
protocol

Hammiefilter / sb_filter
A simple mail filter suitable for embedding in a procmail
environment

That's great, but what's SpamBayes?

(the non-technical hand-waving answer)

SpamBayes will attempt to classify incoming email messages as 'spam',
'ham' (good, non-spam email) or 'unsure'. This means you can have spam
or unsure messages automatically filed away in a different mail folder,
where it won't interrupt your email reading. First SpamBayes must be
trained by each user to identify spam
and ham.

Essentially, you show SpamBayes a pile of email that you like (ham)
and a pile you don't like (spam).

SpamBayes will then analyze the piles for clues as to what makes the
spam and ham different. For example; different words, differences in the
mailer headers and content style. The system then uses these clues to
examine new messages.

For instance, the word "Nigeria" appears often in spam, so
you could use a spam filter which identifies anything with that word in
it as spam. But what if your business involves writing a guidebook on
Nigerian Wildlife Conservation?
Clearly a more flexible approach is necessary.

Additionally spammers
will adapt their content over time and will no longer use the word
"Nigeria" (or the words "Lose Weight Fast", or any number of other
common lines). Ideally the software will be able to adapt as the spam changes.

So, that
is what SpamBayes does. It compares the spam and the ham
and calculates probabilities. For instance, for me, the word "weight"
almost never occurs in legitimate email, but it occurs all the time in
'lose weight fast' spam.

SpamBayes can then look at incoming email, extract the most significant
clues and combine the probabilities to produce an overall rating of
"spamminess". It flags the messages so that your mailer can handle
the different message types. You might set it up so that ham goes
straight through untouched, spam goes to a folder that you ignore (or
delete without checking) and the unsure messages go to another folder
which you can review for errors.

How is SpamBayes different?

There are a number of similar projects
to SpamBayes - most are just using the original Paul Graham algorithm.
Examining the Graham technique with careful testing showed that it did a
remarkably good job, but there was considerable room for improvement.
(See the background page for more.)

The SpamBayes team tinkered with new
algorithms, tweaking existing algorithms, and, most importantly, did
enormous test runs, slamming tens of thousands of messages against
each other, in an attempt to quantify whether or
not a change to the system was beneficial.

The new algorithm is
a combination of work from Gary Robinson and Tim Peters, and provides
not just a 'spam' and 'ham' rating, but also an 'unsure' rating, for
those messages where it can't work out how to rate the message.

See the background page for more, well,
background.

The code (implemented in Python) is currently available from a variety
of methods from the downloads page.

There are now a couple of end-user applications available for those
excited by the bleeding edge - these are detailed on the
Applications page, and available as
part of the source download.

Credits

Most of the heavy lifting on this project was done by Tim Peters, with
the cast of spambayes obsessive-compulsives providing ideas, heckling, and
testing. Gary Robinson provided a lot of the serious maths and theory, as
well as his essay on "how to do it better" (see the
background page for a link). Rob Hooft
also contributed maths/stats clues.
Mark Hammond amazed the world with the Outlook2000 plug-in (with Tony Meyer,
Sean True, and Adam Walker making significant contributions),
and Richie Hindle, Neale Pickett, Tim Stone worked on the end-user applications.

(Thanks also to Rachel Holkner for turning Anthony's gibberish into something
closer to actual English, although all mistakes are Anthony's.)

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