Abstract: A method of storing information relating to the transmission of messages by an entity over a given time period comprises the step of creating a signature comprising a plurality of parameters related to the transmission of messages over that time period wherein the parameters comprise at least one parameter related to the transmission of messages over a portion of the period and also related to the position of the portion in the period, to enable output data to be derived from the stored information. The signature may be updated by a weighted averaging process with other more recent signatures. Application in fraud detection where signature representing information in many call detail records from a particular subscriber is fed to a neural network.

Abstract: A method of processing data relating to a plurality of examples using a data classifier arranged to classify input data into one of a number of classes, and a rule inducer, comprising the steps of: (i) inputting a series of inputs to the data classifier so as to obtain a series of corresponding outputs; (ii) inputting said series of outputs and at least some of said series of inputs to the rule inducer so as to obtain a series of rules which describe relationships between the series of inputs to the data classifier and the series of corresponding outputs from the data classifier.

Abstract: A method of processing data relating to a plurality of examples using a data classifier arranged to classify input data into one of a number of classes, and a rule inducer, comprising the steps of: (i) inputting a series of inputs to the data classifier so as to obtain a series of corresponding outputs; (ii) inputting said series of outputs and at least some of said series of inputs to the rule inducer so as to obtain a series of rules which describe relationships between the series of inputs to the data classifier and the series of corresponding outputs from 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 of processing data relating to a plurality of examples using a data classifier arranged to classify input data into one of a number of classes, and a rule inducer, comprising the steps of:

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.