초록 ▼

A probabilistic learning element particularly adapted for use as a task independent sequential pattern recognition device receives sequences of objects and outputs sequences of recognized states composed of objects and includes a plurality of memories for storing the received sequences of objects an

A probabilistic learning element particularly adapted for use as a task independent sequential pattern recognition device receives sequences of objects and outputs sequences of recognized states composed of objects and includes a plurality of memories for storing the received sequences of objects and previously learned states as well as predetermined types of knowledge relating to previously learned states. The sequences of received objects are correlated with the information relating to the previously learned states in order to assign probabilities to possible next states in the sequence of recognized states. Based upon the probabilities of the possible next states the most likely next state is determined and outputted as a recognized next state in the recognized state sequence when the element determines that a state has ended. The element additionally includes means for providing a rating of confidence in the recognized next state. The ratings of confidence for a sequence of recognized stated are accumulated and if the accumulated value exceeds a predetermined threshold level the element will be caused to store the recognized state sequence as a learned state sequence.

대표청구항 ▼

A probabilistic learning element, that sequentially receives objects and outputs sequences of recognized states, said learning element comprising: means for sequentially receiving objects; means for storing, said received objects, sequences of received objects, previously learned sequences of states

A probabilistic learning element, that sequentially receives objects and outputs sequences of recognized states, said learning element comprising: means for sequentially receiving objects; means for storing, said received objects, sequences of received objects, previously learned sequences of states, states contained in said previously learned sequences of states, and predetermined types of knowledge relating to, said previously learned sequences of states, said states contained in said previously learned sequences of states, objects contained in said previously learned sequences of states, and sequences of objects contained in said previously learned sequences of states, whereby current object information relating to said received objects and said sequences of received objects is stored as well as statistical information relating to previously learned sequences of states and said states, objects and sequences of objects contained in said previously learned sequences of states; means for correlating said stored current object information with said stored statistical information for assigning probabilities to possible next states in the sequence of recognized states; means, responsive to said probabilities of possible next states, for determining a most likely next state; means, responsive to the stored current object information and statistical information, for providing a signal corresponding to the probability that a state has ended; and means, responsive to said end of state signal, for outputting said most likely next state as a recognized next state in a recognized state sequence.