Word spam generator

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Bayesian spam filters calculate the probability of a message being spam based on its contents. Unlike simple word-based filters, Bayesian spam filters learn from incoming spam and good email, resulting in a very robust, adaptive, efficient anti-spam approach that rarely returns false positives.Email messages not considered spam are sometimes referred to as "ham." Bayesian Filters Keep Getting Better Simple word-based spam filters don't consider what might be considered unusual words (one clue that a given message might be spam) for each email user. In addition, they don't have the capacity to change the rules they use to identify spam over time. Bayesian spam filters are different in that they do both. Bayesian spam filters build a list of unwanted words over time. They analyze both spam messages and good messages to calculate the probability of various characteristics appearing in spam, and in good mail. Then new, unwanted words are added to the list. If a word never appears in spam but often in the legitimate email you receive, the probability that word indicates spam is near zero. For example, say you receive many legitimate messages that contain the word Cartesian. That fact decreases the likelihood that email messages you receive containing the word Cartesian are spam. On the other hand, say you rarely or ever receive legitimate messages that contain the word toner. If you receive a message that does contain the word toner, it's likelier to be spam. How a Bayesian Filter Examines an Email Message Message characteristics a Bayesian spam filter looks at include:Words in the body of the messageWords in the message header (such as the sender and message path)Other elements such as HTML/CSS code (such as colors and other formatting)Word pairs and phrasesMeta information (such as where a particular phrase appears) When a new message arrives, the Bayesian spam filter analyzes it and calculates the probability of it being spam according to these attributes. Continuing with the examples above, suppose a message contains both words, Cartesian and toner. From these words alone it's not clear whether the message is spam or legitimate email. But if the message also contains the header "GREAT DEALS ON TONER!!!!!" then the likelihood of it being spam is increased. Bayesian Filters Automatically Learn Following the classification into "spam" or "legitimate email," the filter can use that determination to further train itself. In our example, the filter must either lower the probability of Cartesian indicating good mail or raise the probability of toner indicating spam. Given the additional data of the spammy header on this message (and perhaps other factors as well), it would do the latter and evaluate the next incoming message based on the new probability. Using this auto-adaptive technique,

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