mail::spamassassin::message::metadata(3) [suse man page]

Mail::SpamAssassin::Message::Metadata(3)		User Contributed Perl Documentation		  Mail::SpamAssassin::Message::Metadata(3)

NAME

       Mail::SpamAssassin::Message::Metadata - extract metadata from a message

SYNOPSIS

DESCRIPTION

       This class is tasked with extracting "metadata" from messages for use as Bayes tokens, fodder for eval tests, or other rules.  Metadata is
       supplemental data inferred from the message, like the examples below.

       It is held in two forms:

       1. as name-value pairs of strings, presented in mail header format.  For
	 example, "X-Language" => "en".  This is the general form for simple
	 metadata that's useful as Bayes tokens, can be added to marked-up
	 messages using "add_header", etc., such as the trusted-relay inference
	 and language detection.

       2. as more complex data structures on the $msg->{metadata} object.  This
	 is the form used for metadata like the HTML parse data, which is stored
	 there for access by eval rule code.   Because it's not simple strings,
	 it's not added as a Bayes token by default (Bayes needs simple strings).

PUBLIC METHODS

       new()

perl v5.12.1							    2010-03-16				  Mail::SpamAssassin::Message::Metadata(3)

Mail::SpamAssassin::Plugin::AutoLearnThreshold(3)	User Contributed Perl Documentation	 Mail::SpamAssassin::Plugin::AutoLearnThreshold(3)

NAME

       Mail::SpamAssassin::Plugin::AutoLearnThreshold - threshold-based discriminator for Bayes auto-learning

SYNOPSIS

	 loadplugin	Mail::SpamAssassin::Plugin::AutoLearnThreshold

DESCRIPTION

       This plugin implements the threshold-based auto-learning discriminator for SpamAssassin's Bayes subsystem.  Auto-learning is a mechanism
       whereby high-scoring mails (or low-scoring mails, for non-spam) are fed into its learning systems without user intervention, during
       scanning.

       Note that certain tests are ignored when determining whether a message should be trained upon:

       o   rules with tflags set to 'learn' (the Bayesian rules)

       o   rules with tflags set to 'userconf' (user configuration)

       o   rules with tflags set to 'noautolearn'

       Also note that auto-learning occurs using scores from either scoreset 0 or 1, depending on what scoreset is used during message check.  It
       is likely that the message check and auto-learn scores will be different.

USER OPTIONS

       The following configuration settings are used to control auto-learning:

       bayes_auto_learn_threshold_nonspam n.nn	 (default: 0.1)
	   The score threshold below which a mail has to score, to be fed into SpamAssassin's learning systems automatically as a non-spam
	   message.

       bayes_auto_learn_threshold_spam n.nn	 (default: 12.0)
	   The score threshold above which a mail has to score, to be fed into SpamAssassin's learning systems automatically as a spam message.

	   Note: SpamAssassin requires at least 3 points from the header, and 3 points from the body to auto-learn as spam.  Therefore, the
	   minimum working value for this option is 6.

       bayes_auto_learn_on_error (0 | 1)	(default: 0)
	   With "bayes_auto_learn_on_error" off, autolearning will be performed even if bayes classifier already agrees with the new
	   classification (i.e.  yielded BAYES_00 for what we are now trying to teach it as ham, or yielded BAYES_99 for spam). This is a
	   traditional setting, the default was chosen to retain backwards compatibility.

	   With "bayes_auto_learn_on_error" turned on, autolearning will be performed only when a bayes classifier had a different opinion from
	   what the autolearner is now trying to teach it (i.e. it made an error in judgement). This strategy may or may not produce better future
	   classifications, but usually works very well, while also preventing unnecessary overlearning and slows down database growth.

perl v5.16.3							    2011-06-06			 Mail::SpamAssassin::Plugin::AutoLearnThreshold(3)