while no-one passes it, that message will start receiving higher and higher spam scores as it is delivered to other users.

Another beauty of a bayesian filter is that most of the time you will never know it is there. It cleans your incoming mail before you ever see it, leaving you with (mostly) non spam messages in your inbox after taking out all the (mostly)

Those who use webmails such as Yahoo, Gmail and hotmail will have noticed a spam message folder. This is the folder where messages the email provider suspects as spam are sent, stored for a month or so and then deleted. Most of the time people will not go into this folder at all, because the messages they do want are directed the the inbox. Sometimes however messages are falsely labelled as spam and dropped into this folder, and unless the user is specifically expecting that message, they usually won't even know.

The program that does this automatic sorting with such a high success rate is known as a Bayesian Filter, and it actually runs a series of ingenious tests on the titles and content of each email it sees to determine it's status. Words are assigned a score based on their 'spamliness' and the entire message is then given a 'spam percentage'. Where bayesian Filters are so effective is that they can learn what sort of mail you like and don't like and personalise themselves to you. They then allow you to choose the level of security you would like. The levels are basically defined by what score of 'spam percentage' constitutes spam. For instance if the filter detects a message is 50% likely to be spam this may be flagged as spam on high level security, but not on Medium level.

Webmails have the added advantage of having a huge pool of customers information to draw from. If the first 50 people to receive a message flag it as spam while no-one passes it, that message will start receiving higher and higher spam scores as it is delivered to other users.

Another beauty of a bayesian filter is that most of the time you will never know it is there. It cleans your incoming mail before you ever see it, leaving you with (mostly) non spam messages in your inbox after taking out all the (mostly) s

ges are falsely labelled as spam and dropped into this folder, and unless the user is specifically expecting that message, they usually won't even know.

The program that does this automatic sorting with such a high success rate is known as a Bayesian Filter, and it actually runs a series of ingenious tests on the titles and content of each email it sees to determine it's status. Words are assigned a score based on their 'spamliness' and the entire message is then given a 'spam percentage'. Where bayesian Filters are so effective is that they can learn what sort of mail you like and don't like and personalise themselves to you. They then allow you to choose the level of security you would like. The levels are basically defined by what score of 'spam percentage' constitutes spam. For instance if the filter detects a message is 50% likely to be spam this may be flagged as spam on high level security, but not on Medium level.

Webmails have the added advantage of having a huge pool of customers information to draw from. If the first 50 people to receive a message flag it as spam while no-one passes it, that message will start receiving higher and higher spam scores as it is delivered to other users.

Another beauty of a bayesian filter is that most of the time you will never know it is there. It cleans your incoming mail before you ever see it, leaving you with (mostly) non spam messages in your inbox after taking out all the (mostly)

mine it's status. Words are assigned a score based on their 'spamliness' and the entire message is then given a 'spam percentage'. Where bayesian Filters are so effective is that they can learn what sort of mail you like and don't like and personalise themselves to you. They then allow you to choose the level of security you would like. The levels are basically defined by what score of 'spam percentage' constitutes spam. For instance if the filter detects a message is 50% likely to be spam this may be flagged as spam on high level security, but not on Medium level.

Webmails have the added advantage of having a huge pool of customers information to draw from. If the first 50 people to receive a message flag it as spam while no-one passes it, that message will start receiving higher and higher spam scores as it is delivered to other users.

Another beauty of a bayesian filter is that most of the time you will never know it is there. It cleans your incoming mail before you ever see it, leaving you with (mostly) non spam messages in your inbox after taking out all the (mostly)

efined by what score of 'spam percentage' constitutes spam. For instance if the filter detects a message is 50% likely to be spam this may be flagged as spam on high level security, but not on Medium level.

Webmails have the added advantage of having a huge pool of customers information to draw from. If the first 50 people to receive a message flag it as spam while no-one passes it, that message will start receiving higher and higher spam scores as it is delivered to other users.

Another beauty of a bayesian filter is that most of the time you will never know it is there. It cleans your incoming mail before you ever see it, leaving you with (mostly) non spam messages in your inbox after taking out all the (mostly)

while no-one passes it, that message will start receiving higher and higher spam scores as it is delivered to other users.

Another beauty of a bayesian filter is that most of the time you will never know it is there. It cleans your incoming mail before you ever see it, leaving you with (mostly) non spam messages in your inbox after taking out all the (mostly) spam.