초록 ▼

A method and apparatus for determining the accuracy limit of a learning machine for predicting path performance degradation imposed by the quality of the path performance data is disclosed. A plurality of learning machines of increasing capacity are trained using training data and tested using test

A method and apparatus for determining the accuracy limit of a learning machine for predicting path performance degradation imposed by the quality of the path performance data is disclosed. A plurality of learning machines of increasing capacity are trained using training data and tested using test data, and the training error rates and test error rates are calculated. The asymptotic error rates of the learning machines are calculated and compared. When the change in asymptotic error rate falls below a certain rate, the asymptotic error rate estimates the accuracy limit for a learning machine for predicting path performance degradation. The accuracy limit is derived from insufficiencies in the path performance data and is applicable to any learning machine trained on and applied to the path performance data, regardless of the complexity of the learning machine or the size of the training data set.

대표청구항 ▼

[ We claim:] [1.] A method for determining an accuracy limit of a learning machine for predicting path performance degradation in a telecommunications network, said learning machine analyzing a performance data set for a path having an intrinsic noise level, said path performance data set comprising

[ We claim:] [1.] A method for determining an accuracy limit of a learning machine for predicting path performance degradation in a telecommunications network, said learning machine analyzing a performance data set for a path having an intrinsic noise level, said path performance data set comprising a training data set and a test data set, the method comprising the steps of:a) training a learning machine having a capacity to produce a classification of said path performance data set using said training data set and calculating a training error, said classification indicating whether said path will likely exhibit a performance degradation in the future;b) testing said learning machine using said test data set and calculating a test error;c) calculating an asymptotic error rate based on said test error and said training error;d) determining whether a stop condition has been satisfied; ande) if the stop condition is not satisfied, repeating steps a) through d) until said stop condition is satisfied, wherein each repetition of said steps is performed in conjunction with a learning machine having a capacity greater than that of the previous repetition of said steps.