Resources Contact Us Home
Spam filtering using feature relevance assignment in neural networks

Image Number 11 for United States Patent #8131655.

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.

  Recently Added Patents
Scoring records for sorting by user-specific weights based on relative importance
Reducing voltage stress in a flyback converter design
Channel marking for chip mark overflow and calibration errors
Event-triggered server-side macros
Network-based dynamic encoding
Active gate drive circuit
Collaborative system for capture and reuse of software application knowledge and a method of realizing same
  Randomly Featured Patents
Method and system for generating a billing record
Method for making a low-noise bipolar transistor
Thread-forming screw
Fuel tank filler pipe arrangement
Method for making a carbon-reinforced electrode
Method of triggering a film containing an oxygen scavenger
Steering mechanism for bi-directional catheter
Folding ironing board with push arm
Method for preparation of ethers