| 摘要 |
In some embodiments, a spam filtering method includes computing a pattern relevance for each of a set of message feature patterns, and using a neural network filter to classify incoming messages as spam or ham according to the pattern relevancies. Each message feature pattern is characterized by the simultaneous presence within a message of a specific set of message features (e.g., the presence of certain keywords within the message body, various message header heuristics, various message layout features, etc.). Each message feature may be spam- or ham-identifying, and may receive a tunable feature relevance weight from an external source (e.g. data file and/or human operator). The external feature relevance weights modulate the set of neuronal weights calculated through a training process of the neural network. |
