Abstract: A method for operating a data classifier operable to generate an element of output data in response to an element of input data, such as a neural network, is disclosed. The method includes using the data classifier to generate elements of result output data in response to elements of test input data, generating a measure of difference between each element of test output data and each corresponding element of result output data, associating the measures of difference with categories corresponding to different values of measures of difference, and, based on the number of measures of difference associated with the categories, generating a performance measure of the data classifier.

Abstract: A method for assessing the performance of a data classifier operable to generate an element of output data in response to an element of input data, such as a neural network, is disclosed. The method includes steps of using the data classifier to generate elements of result output data in response to elements of test input data, determining a measure of difference between each element of test output data and each corresponding element of result output data, forming a distribution function of said measures of difference and forming a measure of performance from said distribution function.

Abstract: A method for assessing the performance of a data classifier operable to generate an element of output data in response to an element of input data, such as a neural network, is disclosed. The method includes using the data classifier to generate elements of result output data in response to elements of test input data, determining a measure of difference between each element of test output data and each corresponding element of result output data, forming a distribution function of the measures of differences, and forming a measure of performance from the distribution function.

Abstract: A method and related apparatus and software of detecting anomalies in a stream of data values. The method comprises the steps of: receiving a data value on the stream of data; calculating a new weighted average responsive to the received data value, a previously stored weighted average associated with the stream of data, and a decay rate in the range of 0 to 1; and calculating a new measure of deviation from the new weighted average responsive to the new weighted average, the data value, a previously stored measure of deviation associated with the stream of data, and the decay rate.

Abstract: A method for assessing the performance of a data classifier operable to generate an element of output data in response to an element of input data, such as a neural network, is disclosed. The method includes steps of using the data classifier to generate elements of result output data in response to elements of test input data, determining a measure of difference between each element of test output data and each corresponding element of result output data, forming a distribution function of said measures of difference and forming a measure of performance from said distribution function.

Abstract: A method and apparatus are provided for forming a measure of difference between two data vectors, in particular for use in a trainable data classifier system. An association coefficient determined for the two vectors is used to form the measure of difference. A geometric difference between the two vectors may advantageously be combined with the association coefficient in forming the measure of difference. A particular application is the determination of conflicts between items of training data proposed for use in training a neural network to detect telecommunications account fraud or network intrusion.

Abstract: A method and apparatus is provided for retraining a trainable data classifier (for example, a neural network). Data provided for retraining the classifier is compared with training data previously used to train the classifier, and a measure of the degree of conflict between the new and old training data is calculated. This measure is compared with a predetermined threshold to determine whether the new data should be used in retraining the data classifier. New training data which is found to conflict with earlier data may be further reviewed manually for inclusion.