From 49e7dfad28137bf1e7152640590d9a073331e60a Mon Sep 17 00:00:00 2001
From: yamaoka <yamaoka>
Date: Thu, 21 Nov 2002 22:30:57 +0000
Subject: [PATCH] Synch with Oort Gnus (gnus-ja.texi hasn't been translated
 yet).

---
 lisp/ChangeLog    |    7 +
 lisp/spam.el      |    8 +-
 texi/ChangeLog    |   28 +++
 texi/gnus-ja.texi |  594 ++++++++++++++++++++++++++++++++++++++++++++++++++++-
 texi/gnus.texi    |  594 ++++++++++++++++++++++++++++++++++++++++++++++++++++-
 5 files changed, 1226 insertions(+), 5 deletions(-)

diff --git a/lisp/ChangeLog b/lisp/ChangeLog
index ff5353a..23aa57d 100644
--- a/lisp/ChangeLog
+++ b/lisp/ChangeLog
@@ -1,3 +1,10 @@
+2002-11-21  Teodor Zlatanov  <tzz@lifelogs.com>
+
+	* spam.el:
+	added patch from Andreas Fuchs <asf@void.at> to prevent apply errors
+
+	* spam.el: added `M s t' and `M s x' key mappings
+
 2002-11-20  Simon Josefsson  <jas@extundo.com>
 
 	* gnus-sum.el (gnus-summary-morse-message): Narrow to body.
diff --git a/lisp/spam.el b/lisp/spam.el
index 639571e..bb399c4 100644
--- a/lisp/spam.el
+++ b/lisp/spam.el
@@ -131,6 +131,8 @@ Such articles will be transmitted to `bogofilter -s' on group exit.")
 (gnus-define-keys gnus-summary-mode-map
   "St" spam-bogofilter-score
   "Sx" gnus-summary-mark-as-spam
+  "Mst" spam-bogofilter-score
+  "Msx" gnus-summary-mark-as-spam
   "\M-d" gnus-summary-mark-as-spam)
 
 ;;; How to highlight a spam summary line.
@@ -195,8 +197,8 @@ See the Info node `(gnus)Fancy Mail Splitting' for more details."
 	decision)
     (while (and list-of-checks (not decision))
       (let ((pair (pop list-of-checks)))
-	(when (eval (car pair))
-	  (setq decision (apply (cdr pair))))))
+	(when (symbol-value (car pair))
+	  (setq decision (funcall (cdr pair))))))
     (if (eq decision t)
 	nil
       decision)))
@@ -374,7 +376,7 @@ The regular expression is matched against the address.")
 ;;;     make install
 ;;;
 ;;; Here as well, you need to become super-user for the last step.  Now,
-;;; initialises your word lists by doing, under your own identity:
+;;; initialize your word lists by doing, under your own identity:
 ;;;
 ;;;     mkdir ~/.bogofilter
 ;;;     touch ~/.bogofilter/badlist
diff --git a/texi/ChangeLog b/texi/ChangeLog
index 1b6f32e..170a2be 100644
--- a/texi/ChangeLog
+++ b/texi/ChangeLog
@@ -1,3 +1,31 @@
+2002-11-21  Teodor Zlatanov  <tzz@lifelogs.com>
+
+	* gnus.texi:
+	added new keyboard commands
+
+	* gnus.texi: added extended section on spam
+
+2002-11-18  jas
+
+	* gnus.texi: Fix IMAP expiring typos.
+
+2002-11-18  kaig
+
+	* gnus.texi: *** empty log message ***
+
+2002-11-18  jas
+
+	* gnus.texi: More morse.
+
+	* gnus.texi (Article Washing): Add morse.
+
+2002-11-17  jas
+
+	* gnus.texi: Fix typo.
+
+	* gnus.texi (Expiring in IMAP): Add.
+	(Group Parameters): Add reference.
+
 2002-11-16  Kai Gro,A_(Bjohann  <kai.grossjohann@uni-duisburg.de>
 
 	* gnus.texi (Expiring Mail): Give summary on difference between
diff --git a/texi/gnus-ja.texi b/texi/gnus-ja.texi
index 204569e..4c345fc 100644
--- a/texi/gnus-ja.texi
+++ b/texi/gnus-ja.texi
@@ -976,9 +976,12 @@ Picons
 
 Thwarting Email Spam
 
+* The problem of spam::         $BGX7J!"$=$7$F2r7h(B
 * Anti-Spam Basics::            $B$?$/$5$s$N(B spam $B$r8:$i$94JC1$JJ}K!(B
 * SpamAssassin::                Spam $BBP:v%D!<%k$N;H$$J}(B
 * Hashcash::                    CPU $B;~4V$rHq$d$7$F(B spam $BB`<#$9$k(B
+* Filtering Spam Using spam.el::  
+* Filtering Spam Using Statistics (spam-stat.el)::  
 
 Appendices
 
@@ -20545,14 +20548,85 @@ Gnus $B$O!"%9%3%"IU$1!"%9%l%C%I$N7A@.!"%9%l%C%IHf3S$J$I$r9T$&$H$-$K!"(B
 $B9p(B (``$B:G?7(B! $B4q@W$NA}LS%H%K%C%/!"$U$5$U$5$G$D$d$D$d$NH1$r!"$"$J$?$N$D$^@h(B
 $B$^$G(B!'') $B$H!"2y$$2~$a?@$r?.$8$h!"$H$$$&0l$D$N%a!<%k$,$"$k$@$1$J$N$G$9!#(B
 
-$B$3$l$OITL{2w$G$9!#(B
+$B$3$l$OITL{2w$G$9!#$"$J$?$,$=$l$K4X$7$F$G$-$k$3$H$,$"$j$^$9!#(B
 
 @menu
+* The problem of spam::         $BGX7J!"$=$7$F2r7h(B
 * Anti-Spam Basics::            $B$?$/$5$s$N(B spam $B$r8:$i$94JC1$JJ}K!(B
 * SpamAssassin::                Spam $BBP:v%D!<%k$N;H$$J}(B
 * Hashcash::                    CPU $B;~4V$rHq$d$7$F(B spam $BB`<#$9$k(B
+* Filtering Spam Using spam.el::  
+* Filtering Spam Using Statistics (spam-stat.el)::  
 @end menu
 
+@node The problem of spam
+@subsection The problem of spam
+@cindex email spam
+@cindex spam filtering approaches
+@cindex filtering approaches, spam
+@cindex UCE
+@cindex unsolicited commercial email
+
+First, some background on spam.
+
+If you have access to e-mail, you are familiar with spam (technically
+termed @acronym{UCE}, Unsolicited Commercial E-mail).  Simply put, it exists
+because e-mail delivery is very cheap compared to paper mail, so only
+a very small percentage of people need to respond to an UCE to make it
+worthwhile to the advertiser.  Ironically, one of the most common
+spams is the one offering a database of e-mail addresses for further
+spamming.  Senders of spam are usually called @emph{spammers}, but terms like
+@emph{vermin}, @emph{scum}, and @emph{morons} are in common use as well.
+
+Spam comes from a wide variety of sources.  It is simply impossible to
+dispose of all spam without discarding useful messages.  A good
+example is the TMDA system, which requires senders
+unknown to you to confirm themselves as legitimate senders before
+their e-mail can reach you.  Without getting into the technical side
+of TMDA, a downside is clearly that e-mail from legitimate sources may
+be discarded if those sources can't or won't confirm themselves
+through the TMDA system.  Another problem with TMDA is that it
+requires its users to have a basic understanding of e-mail delivery
+and processing.
+
+The simplest approach to filtering spam is filtering.  If you get 200
+spam messages per day from @email{random-address@@vmadmin.com}, you
+block @samp{vmadmin.com}.  If you get 200 messages about
+@samp{VIAGRA}, you discard all messages with @samp{VIAGRA} in the
+message.  This, unfortunately, is a great way to discard legitimate
+e-mail.  For instance, the very informative and useful RISKS digest
+has been blocked by overzealous mail filters because it
+@strong{contained} words that were common in spam messages.
+Nevertheless, in isolated cases, with great care, direct filtering of
+mail can be useful.
+
+Another approach to filtering e-mail is the distributed spam
+processing, for instance DCC implements such a system.  In essence,
+@code{N} systems around the world agree that a machine @samp{X} in
+China, Ghana, or California is sending out spam e-mail, and these
+@code{N} systems enter @samp{X} or the spam e-mail from @samp{X} into
+a database.  The criteria for spam detection vary - it may be the
+number of messages sent, the content of the messages, and so on.  When
+a user of the distributed processing system wants to find out if a
+message is spam, he consults one of those @code{N} systems.
+
+Distributed spam processing works very well against spammers that send
+a large number of messages at once, but it requires the user to set up
+fairly complicated checks.  There are commercial and free distributed
+spam processing systems.  Distributed spam processing has its risks as
+well.  For instance legitimate e-mail senders have been accused of
+sending spam, and their web sites have been shut down for some time
+because of the incident.
+
+The statistical approach to spam filtering is also popular.  It is
+based on a statistical analysis of previous spam messages.  Usually
+the analysis is a simple word frequency count, with perhaps pairs or
+words or 3-word combinations thrown into the mix.  Statistical
+analysis of spam works very well in most of the cases, but it can
+classify legitimate e-mail as spam in some cases.  It takes time to
+run the analysis, the full message must be analyzed, and the user has
+to store the database of spam analyses.
+
 @node Anti-Spam Basics
 @subsection Spam $BB`<#$N4pAC(B
 @cindex email spam
@@ -20773,6 +20847,524 @@ Spam $B$H@o$&$?$a$N?7$7$$5;K!$O!"%a%C%;!<%8$rAw?.$9$k:]$K$$$/$P$/$+$NIiC4(B
 $B<j:n6H$G%a!<%k$r$U$k$$$K$+$1$k%9%/%j%W%H$r%+%9%?%^%$%:$9$k$3$H$,5a$a$i$l(B
 $B$F$$$^$9!#$7$+$7!"$3$NJ,Ln$K$*$1$k2~NI$OM-MQ$J9W8%$K$J$k$G$7$g$&!#(B
 
+@node Filtering Spam Using spam.el
+@subsection Filtering Spam Using spam.el
+@cindex spam filtering
+@cindex spam.el
+
+The idea behind @code{spam.el} is to have a control center for spam detection
+and filtering in Gnus.  To that end, @code{spam.el} does two things: it
+filters incoming mail, and it analyzes mail known to be spam.
+
+So, what happens when you load @code{spam.el}?  First of all, you get
+the following keyboard commands:
+
+@table @kbd
+
+@item M-d
+@itemx M s x
+@itemx S x
+@kindex M-d
+@kindex S x
+@kindex M s x
+@findex gnus-summary-mark-as-spam
+(@code{gnus-summary-mark-as-spam})
+
+Mark current article as spam, showing it with the @samp{H} mark.
+Whenever you see a spam article, make sure to mark its summary line
+with @kbd{M-d} before leaving the group.
+
+@item M s t
+@itemx S t
+@kindex M s t
+@kindex S t
+@findex spam-bogofilter-score
+(@code{spam-bogofilter-score}
+
+You must have bogofilter processing enabled for that command to work
+properly.
+
+@xref{Bogofilter}.
+
+@end table
+
+Gnus can learn from the spam you get.  All you have to do is collect
+your spam in one or more spam groups, and set the variable
+@code{spam-junk-mailgroups} as appropriate.  In these groups, all messages
+are considered to be spam by default: they get the @samp{H} mark.  You must
+review these messages from time to time and remove the @samp{H} mark for
+every message that is not spam after all.  When you leave a a spam
+group, all messages that continue with the @samp{H} mark, are passed on to
+the spam-detection engine (bogofilter, ifile, and others).  To remove
+the @samp{H} mark, you can use @kbd{M-u} to "unread" the article, or @kbd{d} for
+declaring it read the non-spam way.  When you leave a group, all @samp{H}
+marked articles, saved or unsaved, are sent to Bogofilter or ifile
+(depending on @code{spam-use-bogofilter} and @code{spam-use-ifile}), which will study
+them as spam samples.
+
+Messages may also be deleted in various other ways, and unless
+@code{`spam-ham-marks-form} gets overridden below, marks @samp{R} and @samp{r} for
+default read or explicit delete, marks @samp{X} and @samp{K} for automatic or
+explicit kills, as well as mark @samp{Y} for low scores, are all considered
+to be associated with articles which are not spam.  This assumption
+might be false, in particular if you use kill files or score files as
+means for detecting genuine spam, you should then adjust
+@code{spam-ham-marks-form}.  When you leave a group, all _unsaved_ articles
+bearing any the above marks are sent to Bogofilter or ifile, which
+will study these as not-spam samples.  If you explicit kill a lot, you
+might sometimes end up with articles marked @samp{K} which you never saw,
+and which might accidentally contain spam.  Best is to make sure that
+real spam is marked with @samp{H}, and nothing else.
+
+All other marks do not contribute to Bogofilter or ifile
+pre-conditioning.  In particular, ticked, dormant or souped articles
+are likely to contribute later, when they will get deleted for real,
+so there is no need to use them prematurely.  Explicitly expired
+articles do not contribute, command @kbd{E} is a way to get rid of an
+article without Bogofilter or ifile ever seeing it.
+
+@strong{TODO: @code{spam-use-ifile} does not process spam articles on group exit.
+I'm waiting for info from the author of @code{ifile-gnus.el}, because I think
+that functionality should go in @code{ifile-gnus.el} rather than @code{spam.el}.}
+
+To use the @code{spam.el} facilities for incoming mail filtering, you
+must add the following to your fancy split list
+(@code{nnmail-split-fancy} or @code{nnimap-split-fancy}:
+
+@example
+(: spam-split)
+@end example
+
+Note that the fancy split may be called @code{nnmail-split-fancy} or
+@code{nnimap-split-fancy}, depending on whether you use the nnmail or
+nnimap backends to retrieve your mail.
+
+The @code{spam-split} function will process incoming mail and send the mail
+considered to be spam into the group name given by the variable
+@code{spam-split-group}.  Usually that group name is @samp{spam}.
+
+The following are the methods you can use to control the behavior of
+@code{spam-split}:
+
+@menu
+* Blacklists and Whitelists::   
+* BBDB Whitelists::             
+* Blackholes::                  
+* Bogofilter::                  
+* Ifile spam filtering::        
+* Extending spam.el::           
+@end menu
+
+@node Blacklists and Whitelists
+@subsubsection Blacklists and Whitelists
+@cindex spam filtering
+@cindex whitelists, spam filtering
+@cindex blacklists, spam filtering
+@cindex spam.el
+
+@defvar spam-use-blacklist
+Set this variables to t (the default) if you want to use blacklists.
+@end defvar
+
+@defvar spam-use-whitelist
+Set this variables to t if you want to use whitelists.
+@end defvar
+
+Blacklists are lists of regular expressions matching addresses you
+consider to be spam senders.  For instance, to block mail from any
+sender at @samp{vmadmin.com}, you can put @samp{vmadmin.com} in your
+blacklist.  Since you start out with an empty blacklist, no harm is
+done by having the @code{spam-use-blacklist} variable set, so it is
+set by default.  Blacklist entries use the Emacs regular expression
+syntax.
+
+Conversely, whitelists tell Gnus what addresses are considered
+legitimate.  All non-whitelisted addresses are considered spammers.
+This option is probably not useful for most Gnus users unless the
+whitelists is very comprehensive.  Also see @ref{BBDB Whitelists}.
+Whitelist entries use the Emacs regular expression syntax.
+
+The Blacklist and whitelist location can be customized with the
+@code{spam-directory} variable (@file{~/News/spam} by default).  The whitelist
+and blacklist files will be in that directory, named @file{whitelist} and
+@file{blacklist} respectively.
+
+@node BBDB Whitelists
+@subsubsection BBDB Whitelists
+@cindex spam filtering
+@cindex BBDB whitelists, spam filtering
+@cindex BBDB, spam filtering
+@cindex spam.el
+
+@defvar spam-use-bbdb
+
+Analogous to @code{spam-use-whitelist} (@pxref{Blacklists and
+Whitelists}), but uses the BBDB as the source of whitelisted addresses,
+without regular expressions.  You must have the BBDB loaded for
+@code{spam-use-bbdb} to work properly.  Only addresses in the BBDB
+will be allowed through; all others will be classified as spam.
+
+@end defvar
+
+@node Blackholes
+@subsubsection Blackholes
+@cindex spam filtering
+@cindex blackholes, spam filtering
+@cindex spam.el
+
+@defvar spam-use-blackholes
+
+You can let Gnus consult the blackhole-type distributed spam
+processing systems (DCC, for instance) when you set this option.  The
+variable @code{spam-blackhole-servers} holds the list of blackhole servers
+Gnus will consult.
+
+This variable is disabled by default.  It is not recommended at this
+time because of bugs in the @code{dns.el} code.
+
+@end defvar
+
+@node Bogofilter
+@subsubsection Bogofilter
+@cindex spam filtering
+@cindex bogofilter, spam filtering
+@cindex spam.el
+
+@defvar spam-use-bogofilter
+
+Set this variable if you want to use Eric Raymond's speedy Bogofilter.
+This has been tested with a locally patched copy of version 0.4.  Make
+sure to read the installation comments in @code{spam.el}.
+
+With a minimum of care for associating the @samp{H} mark for spam
+articles only, Bogofilter training all gets fairly automatic.  You
+should do this until you get a few hundreds of articles in each
+category, spam or not.  The shell command @command{head -1
+~/.bogofilter/*} shows both article counts.  The command @kbd{S t} in
+summary mode, either for debugging or for curiosity, triggers
+Bogofilter into displaying in another buffer the @emph{spamicity}
+score of the current article (between 0.0 and 1.0), together with the
+article words which most significantly contribute to the score.
+
+@end defvar
+
+@node Ifile spam filtering
+@subsubsection Ifile spam filtering
+@cindex spam filtering
+@cindex ifile, spam filtering
+@cindex spam.el
+
+@defvar spam-use-ifile
+
+Enable this variable if you want to use Ifile, a statistical analyzer
+similar to Bogofilter.  Currently you must have @code{ifile-gnus.el}
+loaded.  The integration of Ifile with @code{spam.el} is not finished
+yet, but you can use @code{ifile-gnus.el} on its own if you like.
+
+@end defvar
+
+@node Extending spam.el
+@subsubsection Extending spam.el
+@cindex spam filtering
+@cindex spam.el, extending
+@cindex extending spam.el
+
+Say you want to add a new backend called blackbox.  Provide the following:
+
+@enumerate
+@item documentation
+
+@item code
+
+@example
+(defvar spam-use-blackbox nil
+  "True if blackbox should be used.")
+@end example
+
+Add
+@example
+    (spam-use-blackbox	 . spam-check-blackbox)
+@end example
+to @code{spam-list-of-checks}.
+
+@item functionality
+Write the @code{spam-check-blackbox} function.  It should return
+@samp{nil} or @code{spam-split-group}.  See the existing
+@code{spam-check-*} functions for examples of what you can do.
+@end enumerate
+
+@node Filtering Spam Using Statistics (spam-stat.el)
+@subsection Filtering Spam Using Statistics (spam-stat.el)
+@cindex Paul Graham
+@cindex Graham, Paul
+@cindex naive Bayesian spam filtering
+@cindex Bayesian spam filtering, naive
+@cindex spam filtering, naive Bayesian
+
+Paul Graham has written an excellent essay about spam filterung using
+statisticts: @uref{http://www.paulgraham.com/spam.html,A Plan for
+Spam}.  In it he describes the inherent deficiency of rule-based
+filtering as used by SpamAssassin, for example: Somebody has to write
+the rules, and everybody else has to install these rules.  You are
+always late.  It would be much better, he argues, to filter mail based
+on wether it somehow resembles spam or non-spam.  One way to measure
+this is word distribution.  He then goes on to describe a solution
+that checks wether a new mail resembles any of your other spam mails
+or not.
+
+The basic idea is this:  Create a two collections of your mail, one
+with spam, one with non-spam.  Count how often each word appears in
+either collection, weight this by the total number of mails in the
+collections, and store this information in a dictionary.  For every
+word in a new mail, determine its probability to belong to a spam or a
+non-spam mail.  Use the 15 most conspicuous words, compute the total
+probability of the mail being spam.  If this probability is higher
+than a certain threshold, the mail is considered to be spam.
+
+Gnus supports this kind of filtering.  But it needs some setting up.
+First, you need two collections of your mail, one with spam, one with
+non-spam.  Then you need to create a dictionary using these two
+collections, and save it.  And last but not least, you need to use
+this dictionary in your fancy mail splitting rules.
+
+@menu
+* Creating a spam-stat dictionary::  
+* Splitting mail using spam-stat::  
+* Low-level interface to the spam-stat dictionary::  
+@end menu
+
+@node Creating a spam-stat dictionary
+@subsubsection Creating a spam-stat dictionary
+
+Before you can begin to filter spam based on statistics, you must
+create these statistics based on two mail collections, one with spam,
+one with non-spam.  These statistics are then stored in a dictionary
+for later use.  In order for these statistics to be meaningfull, you
+need several hundred emails in both collections.
+
+Gnus currently supports only the nnml backend for automated dictionary
+creation.  The nnml backend stores all mails in a directory, one file
+per mail.  Use the following 
+
+@defun spam-stat-process-spam-directory
+Create spam statistics for every file in this directory.  Every file
+is treated as one spam mail.
+@end defun
+
+@defun spam-stat-process-non-spam-directory
+Create non-spam statistics for every file in this directory.  Every
+file is treated as one non-spam mail.
+@end defun
+
+Usually you would call @code{spam-stat-process-spam-directory} on a
+directory such as @file{~/Mail/mail/spam} (this usually corresponds
+the the group @samp{nnml:mail.spam}), and you would call
+@code{spam-stat-process-non-spam-directory} on a directory such as
+@file{~/Mail/mail/misc} (this usually corresponds the the group
+@samp{nnml:mail.misc}).
+
+@defvar spam-stat
+This variable holds the hash-table with all the statistics -- the
+dictionary we have been talking about.  For every word in either
+collection, this hash-table stores a vector describing how often the
+word appeared in spam and often it appeared in non-spam mails.
+
+If you want to regenerate the statistics from scratch, you need to
+reset the dictionary.
+
+@end defvar
+
+@defun spam-stat-reset
+Reset the @code{spam-stat} hash-table, deleting all the statistics.
+
+When you are done, you must save the dictionary.  The dictionary may
+be rather large.  If you will not update the dictionary incrementally
+(instead, you will recreate it once a month, for example), then you
+can reduce the size of the dictionary by deleting all words that did
+not appear often enough or that do not clearly belong to only spam or
+only non-spam mails.
+@end defun
+
+@defun spam-stat-reduce-size
+Reduce the size of the dictionary.  Use this only if you do not want
+to update the dictionary incrementally.
+@end defun
+
+@defun spam-stat-save
+Save the dictionary.
+@end defun
+
+@defvar spam-stat-file
+The filename used to store the dictionary.  This defaults to
+@file{~/.spam-stat.el}.
+@end defvar
+
+@node Splitting mail using spam-stat
+@subsubsection Splitting mail using spam-stat
+
+In order to use @code{spam-stat} to split your mail, you need to add the
+following to your @file{~/.gnus} file:
+
+@example
+(require 'spam-stat)
+(spam-stat-load)
+@end example
+
+This will load the necessary Gnus code, and the dictionary you
+created.
+
+Next, you need to adapt your fancy splitting rules:  You need to
+determine how to use @code{spam-stat}.  In the simplest case, you only have
+two groups, @samp{mail.misc} and @samp{mail.spam}.  The following expression says
+that mail is either spam or it should go into @samp{mail.misc}.  If it is
+spam, then @code{spam-stat-split-fancy} will return @samp{mail.spam}.
+
+@example
+(setq nnmail-split-fancy
+      `(| (: spam-stat-split-fancy)
+	  "mail.misc"))
+@end example
+
+@defvar spam-stat-split-fancy-spam-group
+The group to use for spam.  Default is @samp{mail.spam}.
+@end defvar
+
+If you also filter mail with specific subjects into other groups, use
+the following expression.  It only the mails not matching the regular
+expression are considered potential spam.
+
+@example
+(setq nnmail-split-fancy
+      `(| ("Subject" "\\bspam-stat\\b" "mail.emacs")
+	  (: spam-stat-split-fancy)
+	  "mail.misc"))
+@end example
+
+If you want to filter for spam first, then you must be careful when
+creating the dictionary.  Note that @code{spam-stat-split-fancy} must
+consider both mails in @samp{mail.emacs} and in @samp{mail.misc} as
+non-spam, therefore both should be in your collection of non-spam
+mails, when creating the dictionary!
+
+@example
+(setq nnmail-split-fancy
+      `(| (: spam-stat-split-fancy)
+          ("Subject" "\\bspam-stat\\b" "mail.emacs")
+	  "mail.misc"))
+@end example
+
+You can combine this with traditional filtering.  Here, we move all
+HTML-only mails into the @samp{mail.spam.filtered} group.  Note that since
+@code{spam-stat-split-fancy} will never see them, the mails in
+@samp{mail.spam.filtered} should be neither in your collection of spam mails,
+nor in your collection of non-spam mails, when creating the
+dictionary!
+
+@example
+(setq nnmail-split-fancy
+      `(| ("Content-Type" "text/html" "mail.spam.filtered")
+	  (: spam-stat-split-fancy)
+          ("Subject" "\\bspam-stat\\b" "mail.emacs")
+	  "mail.misc"))
+@end example
+
+
+@node Low-level interface to the spam-stat dictionary
+@subsubsection Low-level interface to the spam-stat dictionary
+
+The main interface to using @code{spam-stat}, are the following functions:
+
+@defun spam-stat-buffer-is-spam
+called in a buffer, that buffer is considered to be a new spam mail;
+use this for new mail that has not been processed before
+
+@end defun
+
+@defun spam-stat-buffer-is-no-spam
+called in a buffer, that buffer is considered to be a new non-spam
+mail; use this for new mail that has not been processed before
+
+@end defun
+
+@defun spam-stat-buffer-change-to-spam
+called in a buffer, that buffer is no longer considered to be normal
+mail but spam; use this to change the status of a mail that has
+already been processed as non-spam
+
+@end defun
+
+@defun spam-stat-buffer-change-to-non-spam
+called in a buffer, that buffer is no longer considered to be spam but
+normal mail; use this to change the status of a mail that has already
+been processed as spam
+
+@end defun
+
+@defun spam-stat-save
+save the hash table to the file; the filename used is stored in the
+variable @code{spam-stat-file}
+
+@end defun
+
+@defun spam-stat-load
+load the hash table from a file; the filename used is stored in the
+variable @code{spam-stat-file}
+
+@end defun
+
+@defun spam-stat-score-word
+return the spam score for a word
+
+@end defun
+
+@defun spam-stat-score-buffer
+return the spam score for a buffer
+
+@end defun
+
+@defun spam-stat-split-fancy
+for fancy mail splitting; add the rule @samp{(: spam-stat-split-fancy)} to
+@code{nnmail-split-fancy}
+
+This requires the following in your @file{~/.gnus} file:
+
+@example
+(require 'spam-stat)
+(spam-stat-load)
+@end example
+
+@end defun
+
+Typical test will involve calls to the following functions:
+
+@example
+Reset: (setq spam-stat (make-hash-table :test 'equal))
+Learn spam: (spam-stat-process-spam-directory "~/Mail/mail/spam")
+Learn non-spam: (spam-stat-process-non-spam-directory "~/Mail/mail/misc")
+Save table: (spam-stat-save)
+File size: (nth 7 (file-attributes spam-stat-file))
+Number of words: (hash-table-count spam-stat)
+Test spam: (spam-stat-test-directory "~/Mail/mail/spam")
+Test non-spam: (spam-stat-test-directory "~/Mail/mail/misc")
+Reduce table size: (spam-stat-reduce-size)
+Save table: (spam-stat-save)
+File size: (nth 7 (file-attributes spam-stat-file))
+Number of words: (hash-table-count spam-stat)
+Test spam: (spam-stat-test-directory "~/Mail/mail/spam")
+Test non-spam: (spam-stat-test-directory "~/Mail/mail/misc")
+@end example
+
+Here is how you would create your dictionary:
+
+@example
+Reset: (setq spam-stat (make-hash-table :test 'equal))
+Learn spam: (spam-stat-process-spam-directory "~/Mail/mail/spam")
+Learn non-spam: (spam-stat-process-non-spam-directory "~/Mail/mail/misc")
+Repeat for any other non-spam group you need...
+Reduce table size: (spam-stat-reduce-size)
+Save table: (spam-stat-save)
+@end example
+
 @node Various Various
 @section $B$$$m$$$m$N$$$m$$$m(B
 @cindex mode lines
diff --git a/texi/gnus.texi b/texi/gnus.texi
index 5b3d9d9..c98aa35 100644
--- a/texi/gnus.texi
+++ b/texi/gnus.texi
@@ -854,9 +854,12 @@ Picons
 
 Thwarting Email Spam
 
+* The problem of spam::         Some background, and some solutions
 * Anti-Spam Basics::            Simple steps to reduce the amount of spam.
 * SpamAssassin::                How to use external anti-spam tools.
 * Hashcash::                    Reduce spam by burning CPU time.
+* Filtering Spam Using spam.el::  
+* Filtering Spam Using Statistics (spam-stat.el)::  
 
 Appendices
 
@@ -20997,14 +21000,85 @@ mail group, only to find two pyramid schemes, seven advertisements
 (``New!  Miracle tonic for growing full, lustrous hair on your toes!'')
 and one mail asking me to repent and find some god.
 
-This is annoying.
+This is annoying.  Here's what you can do about it.
 
 @menu
+* The problem of spam::         Some background, and some solutions
 * Anti-Spam Basics::            Simple steps to reduce the amount of spam.
 * SpamAssassin::                How to use external anti-spam tools.
 * Hashcash::                    Reduce spam by burning CPU time.
+* Filtering Spam Using spam.el::  
+* Filtering Spam Using Statistics (spam-stat.el)::  
 @end menu
 
+@node The problem of spam
+@subsection The problem of spam
+@cindex email spam
+@cindex spam filtering approaches
+@cindex filtering approaches, spam
+@cindex UCE
+@cindex unsolicited commercial email
+
+First, some background on spam.
+
+If you have access to e-mail, you are familiar with spam (technically
+termed @acronym{UCE}, Unsolicited Commercial E-mail).  Simply put, it exists
+because e-mail delivery is very cheap compared to paper mail, so only
+a very small percentage of people need to respond to an UCE to make it
+worthwhile to the advertiser.  Ironically, one of the most common
+spams is the one offering a database of e-mail addresses for further
+spamming.  Senders of spam are usually called @emph{spammers}, but terms like
+@emph{vermin}, @emph{scum}, and @emph{morons} are in common use as well.
+
+Spam comes from a wide variety of sources.  It is simply impossible to
+dispose of all spam without discarding useful messages.  A good
+example is the TMDA system, which requires senders
+unknown to you to confirm themselves as legitimate senders before
+their e-mail can reach you.  Without getting into the technical side
+of TMDA, a downside is clearly that e-mail from legitimate sources may
+be discarded if those sources can't or won't confirm themselves
+through the TMDA system.  Another problem with TMDA is that it
+requires its users to have a basic understanding of e-mail delivery
+and processing.
+
+The simplest approach to filtering spam is filtering.  If you get 200
+spam messages per day from @email{random-address@@vmadmin.com}, you
+block @samp{vmadmin.com}.  If you get 200 messages about
+@samp{VIAGRA}, you discard all messages with @samp{VIAGRA} in the
+message.  This, unfortunately, is a great way to discard legitimate
+e-mail.  For instance, the very informative and useful RISKS digest
+has been blocked by overzealous mail filters because it
+@strong{contained} words that were common in spam messages.
+Nevertheless, in isolated cases, with great care, direct filtering of
+mail can be useful.
+
+Another approach to filtering e-mail is the distributed spam
+processing, for instance DCC implements such a system.  In essence,
+@code{N} systems around the world agree that a machine @samp{X} in
+China, Ghana, or California is sending out spam e-mail, and these
+@code{N} systems enter @samp{X} or the spam e-mail from @samp{X} into
+a database.  The criteria for spam detection vary - it may be the
+number of messages sent, the content of the messages, and so on.  When
+a user of the distributed processing system wants to find out if a
+message is spam, he consults one of those @code{N} systems.
+
+Distributed spam processing works very well against spammers that send
+a large number of messages at once, but it requires the user to set up
+fairly complicated checks.  There are commercial and free distributed
+spam processing systems.  Distributed spam processing has its risks as
+well.  For instance legitimate e-mail senders have been accused of
+sending spam, and their web sites have been shut down for some time
+because of the incident.
+
+The statistical approach to spam filtering is also popular.  It is
+based on a statistical analysis of previous spam messages.  Usually
+the analysis is a simple word frequency count, with perhaps pairs or
+words or 3-word combinations thrown into the mix.  Statistical
+analysis of spam works very well in most of the cases, but it can
+classify legitimate e-mail as spam in some cases.  It takes time to
+run the analysis, the full message must be analyzed, and the user has
+to store the database of spam analyses.
+
 @node Anti-Spam Basics
 @subsection Anti-Spam Basics
 @cindex email spam
@@ -21228,6 +21302,524 @@ hashcash cookies, it is expected that this is performed by your hand
 customized mail filtering scripts.  Improvements in this area would be
 a useful contribution, however.
 
+@node Filtering Spam Using spam.el
+@subsection Filtering Spam Using spam.el
+@cindex spam filtering
+@cindex spam.el
+
+The idea behind @code{spam.el} is to have a control center for spam detection
+and filtering in Gnus.  To that end, @code{spam.el} does two things: it
+filters incoming mail, and it analyzes mail known to be spam.
+
+So, what happens when you load @code{spam.el}?  First of all, you get
+the following keyboard commands:
+
+@table @kbd
+
+@item M-d
+@itemx M s x
+@itemx S x
+@kindex M-d
+@kindex S x
+@kindex M s x
+@findex gnus-summary-mark-as-spam
+(@code{gnus-summary-mark-as-spam})
+
+Mark current article as spam, showing it with the @samp{H} mark.
+Whenever you see a spam article, make sure to mark its summary line
+with @kbd{M-d} before leaving the group.
+
+@item M s t
+@itemx S t
+@kindex M s t
+@kindex S t
+@findex spam-bogofilter-score
+(@code{spam-bogofilter-score}
+
+You must have bogofilter processing enabled for that command to work
+properly.
+
+@xref{Bogofilter}.
+
+@end table
+
+Gnus can learn from the spam you get.  All you have to do is collect
+your spam in one or more spam groups, and set the variable
+@code{spam-junk-mailgroups} as appropriate.  In these groups, all messages
+are considered to be spam by default: they get the @samp{H} mark.  You must
+review these messages from time to time and remove the @samp{H} mark for
+every message that is not spam after all.  When you leave a a spam
+group, all messages that continue with the @samp{H} mark, are passed on to
+the spam-detection engine (bogofilter, ifile, and others).  To remove
+the @samp{H} mark, you can use @kbd{M-u} to "unread" the article, or @kbd{d} for
+declaring it read the non-spam way.  When you leave a group, all @samp{H}
+marked articles, saved or unsaved, are sent to Bogofilter or ifile
+(depending on @code{spam-use-bogofilter} and @code{spam-use-ifile}), which will study
+them as spam samples.
+
+Messages may also be deleted in various other ways, and unless
+@code{`spam-ham-marks-form} gets overridden below, marks @samp{R} and @samp{r} for
+default read or explicit delete, marks @samp{X} and @samp{K} for automatic or
+explicit kills, as well as mark @samp{Y} for low scores, are all considered
+to be associated with articles which are not spam.  This assumption
+might be false, in particular if you use kill files or score files as
+means for detecting genuine spam, you should then adjust
+@code{spam-ham-marks-form}.  When you leave a group, all _unsaved_ articles
+bearing any the above marks are sent to Bogofilter or ifile, which
+will study these as not-spam samples.  If you explicit kill a lot, you
+might sometimes end up with articles marked @samp{K} which you never saw,
+and which might accidentally contain spam.  Best is to make sure that
+real spam is marked with @samp{H}, and nothing else.
+
+All other marks do not contribute to Bogofilter or ifile
+pre-conditioning.  In particular, ticked, dormant or souped articles
+are likely to contribute later, when they will get deleted for real,
+so there is no need to use them prematurely.  Explicitly expired
+articles do not contribute, command @kbd{E} is a way to get rid of an
+article without Bogofilter or ifile ever seeing it.
+
+@strong{TODO: @code{spam-use-ifile} does not process spam articles on group exit.
+I'm waiting for info from the author of @code{ifile-gnus.el}, because I think
+that functionality should go in @code{ifile-gnus.el} rather than @code{spam.el}.}
+
+To use the @code{spam.el} facilities for incoming mail filtering, you
+must add the following to your fancy split list
+(@code{nnmail-split-fancy} or @code{nnimap-split-fancy}:
+
+@example
+(: spam-split)
+@end example
+
+Note that the fancy split may be called @code{nnmail-split-fancy} or
+@code{nnimap-split-fancy}, depending on whether you use the nnmail or
+nnimap backends to retrieve your mail.
+
+The @code{spam-split} function will process incoming mail and send the mail
+considered to be spam into the group name given by the variable
+@code{spam-split-group}.  Usually that group name is @samp{spam}.
+
+The following are the methods you can use to control the behavior of
+@code{spam-split}:
+
+@menu
+* Blacklists and Whitelists::   
+* BBDB Whitelists::             
+* Blackholes::                  
+* Bogofilter::                  
+* Ifile spam filtering::        
+* Extending spam.el::           
+@end menu
+
+@node Blacklists and Whitelists
+@subsubsection Blacklists and Whitelists
+@cindex spam filtering
+@cindex whitelists, spam filtering
+@cindex blacklists, spam filtering
+@cindex spam.el
+
+@defvar spam-use-blacklist
+Set this variables to t (the default) if you want to use blacklists.
+@end defvar
+
+@defvar spam-use-whitelist
+Set this variables to t if you want to use whitelists.
+@end defvar
+
+Blacklists are lists of regular expressions matching addresses you
+consider to be spam senders.  For instance, to block mail from any
+sender at @samp{vmadmin.com}, you can put @samp{vmadmin.com} in your
+blacklist.  Since you start out with an empty blacklist, no harm is
+done by having the @code{spam-use-blacklist} variable set, so it is
+set by default.  Blacklist entries use the Emacs regular expression
+syntax.
+
+Conversely, whitelists tell Gnus what addresses are considered
+legitimate.  All non-whitelisted addresses are considered spammers.
+This option is probably not useful for most Gnus users unless the
+whitelists is very comprehensive.  Also see @ref{BBDB Whitelists}.
+Whitelist entries use the Emacs regular expression syntax.
+
+The Blacklist and whitelist location can be customized with the
+@code{spam-directory} variable (@file{~/News/spam} by default).  The whitelist
+and blacklist files will be in that directory, named @file{whitelist} and
+@file{blacklist} respectively.
+
+@node BBDB Whitelists
+@subsubsection BBDB Whitelists
+@cindex spam filtering
+@cindex BBDB whitelists, spam filtering
+@cindex BBDB, spam filtering
+@cindex spam.el
+
+@defvar spam-use-bbdb
+
+Analogous to @code{spam-use-whitelist} (@pxref{Blacklists and
+Whitelists}), but uses the BBDB as the source of whitelisted addresses,
+without regular expressions.  You must have the BBDB loaded for
+@code{spam-use-bbdb} to work properly.  Only addresses in the BBDB
+will be allowed through; all others will be classified as spam.
+
+@end defvar
+
+@node Blackholes
+@subsubsection Blackholes
+@cindex spam filtering
+@cindex blackholes, spam filtering
+@cindex spam.el
+
+@defvar spam-use-blackholes
+
+You can let Gnus consult the blackhole-type distributed spam
+processing systems (DCC, for instance) when you set this option.  The
+variable @code{spam-blackhole-servers} holds the list of blackhole servers
+Gnus will consult.
+
+This variable is disabled by default.  It is not recommended at this
+time because of bugs in the @code{dns.el} code.
+
+@end defvar
+
+@node Bogofilter
+@subsubsection Bogofilter
+@cindex spam filtering
+@cindex bogofilter, spam filtering
+@cindex spam.el
+
+@defvar spam-use-bogofilter
+
+Set this variable if you want to use Eric Raymond's speedy Bogofilter.
+This has been tested with a locally patched copy of version 0.4.  Make
+sure to read the installation comments in @code{spam.el}.
+
+With a minimum of care for associating the @samp{H} mark for spam
+articles only, Bogofilter training all gets fairly automatic.  You
+should do this until you get a few hundreds of articles in each
+category, spam or not.  The shell command @command{head -1
+~/.bogofilter/*} shows both article counts.  The command @kbd{S t} in
+summary mode, either for debugging or for curiosity, triggers
+Bogofilter into displaying in another buffer the @emph{spamicity}
+score of the current article (between 0.0 and 1.0), together with the
+article words which most significantly contribute to the score.
+
+@end defvar
+
+@node Ifile spam filtering
+@subsubsection Ifile spam filtering
+@cindex spam filtering
+@cindex ifile, spam filtering
+@cindex spam.el
+
+@defvar spam-use-ifile
+
+Enable this variable if you want to use Ifile, a statistical analyzer
+similar to Bogofilter.  Currently you must have @code{ifile-gnus.el}
+loaded.  The integration of Ifile with @code{spam.el} is not finished
+yet, but you can use @code{ifile-gnus.el} on its own if you like.
+
+@end defvar
+
+@node Extending spam.el
+@subsubsection Extending spam.el
+@cindex spam filtering
+@cindex spam.el, extending
+@cindex extending spam.el
+
+Say you want to add a new backend called blackbox.  Provide the following:
+
+@enumerate
+@item documentation
+
+@item code
+
+@example
+(defvar spam-use-blackbox nil
+  "True if blackbox should be used.")
+@end example
+
+Add
+@example
+    (spam-use-blackbox	 . spam-check-blackbox)
+@end example
+to @code{spam-list-of-checks}.
+
+@item functionality
+Write the @code{spam-check-blackbox} function.  It should return
+@samp{nil} or @code{spam-split-group}.  See the existing
+@code{spam-check-*} functions for examples of what you can do.
+@end enumerate
+
+@node Filtering Spam Using Statistics (spam-stat.el)
+@subsection Filtering Spam Using Statistics (spam-stat.el)
+@cindex Paul Graham
+@cindex Graham, Paul
+@cindex naive Bayesian spam filtering
+@cindex Bayesian spam filtering, naive
+@cindex spam filtering, naive Bayesian
+
+Paul Graham has written an excellent essay about spam filterung using
+statisticts: @uref{http://www.paulgraham.com/spam.html,A Plan for
+Spam}.  In it he describes the inherent deficiency of rule-based
+filtering as used by SpamAssassin, for example: Somebody has to write
+the rules, and everybody else has to install these rules.  You are
+always late.  It would be much better, he argues, to filter mail based
+on wether it somehow resembles spam or non-spam.  One way to measure
+this is word distribution.  He then goes on to describe a solution
+that checks wether a new mail resembles any of your other spam mails
+or not.
+
+The basic idea is this:  Create a two collections of your mail, one
+with spam, one with non-spam.  Count how often each word appears in
+either collection, weight this by the total number of mails in the
+collections, and store this information in a dictionary.  For every
+word in a new mail, determine its probability to belong to a spam or a
+non-spam mail.  Use the 15 most conspicuous words, compute the total
+probability of the mail being spam.  If this probability is higher
+than a certain threshold, the mail is considered to be spam.
+
+Gnus supports this kind of filtering.  But it needs some setting up.
+First, you need two collections of your mail, one with spam, one with
+non-spam.  Then you need to create a dictionary using these two
+collections, and save it.  And last but not least, you need to use
+this dictionary in your fancy mail splitting rules.
+
+@menu
+* Creating a spam-stat dictionary::  
+* Splitting mail using spam-stat::  
+* Low-level interface to the spam-stat dictionary::  
+@end menu
+
+@node Creating a spam-stat dictionary
+@subsubsection Creating a spam-stat dictionary
+
+Before you can begin to filter spam based on statistics, you must
+create these statistics based on two mail collections, one with spam,
+one with non-spam.  These statistics are then stored in a dictionary
+for later use.  In order for these statistics to be meaningfull, you
+need several hundred emails in both collections.
+
+Gnus currently supports only the nnml backend for automated dictionary
+creation.  The nnml backend stores all mails in a directory, one file
+per mail.  Use the following 
+
+@defun spam-stat-process-spam-directory
+Create spam statistics for every file in this directory.  Every file
+is treated as one spam mail.
+@end defun
+
+@defun spam-stat-process-non-spam-directory
+Create non-spam statistics for every file in this directory.  Every
+file is treated as one non-spam mail.
+@end defun
+
+Usually you would call @code{spam-stat-process-spam-directory} on a
+directory such as @file{~/Mail/mail/spam} (this usually corresponds
+the the group @samp{nnml:mail.spam}), and you would call
+@code{spam-stat-process-non-spam-directory} on a directory such as
+@file{~/Mail/mail/misc} (this usually corresponds the the group
+@samp{nnml:mail.misc}).
+
+@defvar spam-stat
+This variable holds the hash-table with all the statistics -- the
+dictionary we have been talking about.  For every word in either
+collection, this hash-table stores a vector describing how often the
+word appeared in spam and often it appeared in non-spam mails.
+
+If you want to regenerate the statistics from scratch, you need to
+reset the dictionary.
+
+@end defvar
+
+@defun spam-stat-reset
+Reset the @code{spam-stat} hash-table, deleting all the statistics.
+
+When you are done, you must save the dictionary.  The dictionary may
+be rather large.  If you will not update the dictionary incrementally
+(instead, you will recreate it once a month, for example), then you
+can reduce the size of the dictionary by deleting all words that did
+not appear often enough or that do not clearly belong to only spam or
+only non-spam mails.
+@end defun
+
+@defun spam-stat-reduce-size
+Reduce the size of the dictionary.  Use this only if you do not want
+to update the dictionary incrementally.
+@end defun
+
+@defun spam-stat-save
+Save the dictionary.
+@end defun
+
+@defvar spam-stat-file
+The filename used to store the dictionary.  This defaults to
+@file{~/.spam-stat.el}.
+@end defvar
+
+@node Splitting mail using spam-stat
+@subsubsection Splitting mail using spam-stat
+
+In order to use @code{spam-stat} to split your mail, you need to add the
+following to your @file{~/.gnus} file:
+
+@example
+(require 'spam-stat)
+(spam-stat-load)
+@end example
+
+This will load the necessary Gnus code, and the dictionary you
+created.
+
+Next, you need to adapt your fancy splitting rules:  You need to
+determine how to use @code{spam-stat}.  In the simplest case, you only have
+two groups, @samp{mail.misc} and @samp{mail.spam}.  The following expression says
+that mail is either spam or it should go into @samp{mail.misc}.  If it is
+spam, then @code{spam-stat-split-fancy} will return @samp{mail.spam}.
+
+@example
+(setq nnmail-split-fancy
+      `(| (: spam-stat-split-fancy)
+	  "mail.misc"))
+@end example
+
+@defvar spam-stat-split-fancy-spam-group
+The group to use for spam.  Default is @samp{mail.spam}.
+@end defvar
+
+If you also filter mail with specific subjects into other groups, use
+the following expression.  It only the mails not matching the regular
+expression are considered potential spam.
+
+@example
+(setq nnmail-split-fancy
+      `(| ("Subject" "\\bspam-stat\\b" "mail.emacs")
+	  (: spam-stat-split-fancy)
+	  "mail.misc"))
+@end example
+
+If you want to filter for spam first, then you must be careful when
+creating the dictionary.  Note that @code{spam-stat-split-fancy} must
+consider both mails in @samp{mail.emacs} and in @samp{mail.misc} as
+non-spam, therefore both should be in your collection of non-spam
+mails, when creating the dictionary!
+
+@example
+(setq nnmail-split-fancy
+      `(| (: spam-stat-split-fancy)
+          ("Subject" "\\bspam-stat\\b" "mail.emacs")
+	  "mail.misc"))
+@end example
+
+You can combine this with traditional filtering.  Here, we move all
+HTML-only mails into the @samp{mail.spam.filtered} group.  Note that since
+@code{spam-stat-split-fancy} will never see them, the mails in
+@samp{mail.spam.filtered} should be neither in your collection of spam mails,
+nor in your collection of non-spam mails, when creating the
+dictionary!
+
+@example
+(setq nnmail-split-fancy
+      `(| ("Content-Type" "text/html" "mail.spam.filtered")
+	  (: spam-stat-split-fancy)
+          ("Subject" "\\bspam-stat\\b" "mail.emacs")
+	  "mail.misc"))
+@end example
+
+
+@node Low-level interface to the spam-stat dictionary
+@subsubsection Low-level interface to the spam-stat dictionary
+
+The main interface to using @code{spam-stat}, are the following functions:
+
+@defun spam-stat-buffer-is-spam
+called in a buffer, that buffer is considered to be a new spam mail;
+use this for new mail that has not been processed before
+
+@end defun
+
+@defun spam-stat-buffer-is-no-spam
+called in a buffer, that buffer is considered to be a new non-spam
+mail; use this for new mail that has not been processed before
+
+@end defun
+
+@defun spam-stat-buffer-change-to-spam
+called in a buffer, that buffer is no longer considered to be normal
+mail but spam; use this to change the status of a mail that has
+already been processed as non-spam
+
+@end defun
+
+@defun spam-stat-buffer-change-to-non-spam
+called in a buffer, that buffer is no longer considered to be spam but
+normal mail; use this to change the status of a mail that has already
+been processed as spam
+
+@end defun
+
+@defun spam-stat-save
+save the hash table to the file; the filename used is stored in the
+variable @code{spam-stat-file}
+
+@end defun
+
+@defun spam-stat-load
+load the hash table from a file; the filename used is stored in the
+variable @code{spam-stat-file}
+
+@end defun
+
+@defun spam-stat-score-word
+return the spam score for a word
+
+@end defun
+
+@defun spam-stat-score-buffer
+return the spam score for a buffer
+
+@end defun
+
+@defun spam-stat-split-fancy
+for fancy mail splitting; add the rule @samp{(: spam-stat-split-fancy)} to
+@code{nnmail-split-fancy}
+
+This requires the following in your @file{~/.gnus} file:
+
+@example
+(require 'spam-stat)
+(spam-stat-load)
+@end example
+
+@end defun
+
+Typical test will involve calls to the following functions:
+
+@example
+Reset: (setq spam-stat (make-hash-table :test 'equal))
+Learn spam: (spam-stat-process-spam-directory "~/Mail/mail/spam")
+Learn non-spam: (spam-stat-process-non-spam-directory "~/Mail/mail/misc")
+Save table: (spam-stat-save)
+File size: (nth 7 (file-attributes spam-stat-file))
+Number of words: (hash-table-count spam-stat)
+Test spam: (spam-stat-test-directory "~/Mail/mail/spam")
+Test non-spam: (spam-stat-test-directory "~/Mail/mail/misc")
+Reduce table size: (spam-stat-reduce-size)
+Save table: (spam-stat-save)
+File size: (nth 7 (file-attributes spam-stat-file))
+Number of words: (hash-table-count spam-stat)
+Test spam: (spam-stat-test-directory "~/Mail/mail/spam")
+Test non-spam: (spam-stat-test-directory "~/Mail/mail/misc")
+@end example
+
+Here is how you would create your dictionary:
+
+@example
+Reset: (setq spam-stat (make-hash-table :test 'equal))
+Learn spam: (spam-stat-process-spam-directory "~/Mail/mail/spam")
+Learn non-spam: (spam-stat-process-non-spam-directory "~/Mail/mail/misc")
+Repeat for any other non-spam group you need...
+Reduce table size: (spam-stat-reduce-size)
+Save table: (spam-stat-save)
+@end example
+
 @node Various Various
 @section Various Various
 @cindex mode lines
-- 
1.7.10.4