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.

• In this approach, f can be assumed to be rather noise-free, as long as we want to discriminate spam from non-spam.

• In this domain, the classification tasks to be tackled are twofold: filtering spam is a binary decision, while pre-ordering of mails into existing folders is a more sophisticated learning task.

• As high as the accuracy is, the very cautious can examine the "junk" before deleting it, just in case a wanted message has been incorrectly designated spam.

• In relation to spam, the proposals introduced the possibility for internet service providers to take legal action against spammers.

• The whole idea of opt-in has been put forward as the solution to end spam.

• Unfortunately, the proposal bit the dust, but it would have allowed people to edit spam from their inbox.

• That is where the spam is coming from.