Примеры spam

• A system can be trained to recognize spam messages and delete them or store them at a special place for later scanning.

• The first tries to identify spam mail by the key word cash in the mail subject.

• Most applications here are adaptive spam filters and document classification.

• So, for example, consider a computer program that is designed to learn to classify e-mails as spam or non-spam.

• Disruptive knowledge or 'semantic spam' will also need to be dealt with.

• We could then use a corpus of e-mail messages, each marked as to their spam status, to train the weights appropriately.

• False positives - spam that makes it through the filter - are quickly dealt with, and increase the accuracy of the filter with each use.

• One type is adaptive spam filters that learn to recognize spam from classified e-mail messages.