Abstract: A system and method of determining a neural network configuration may include receiving at least one neural network configuration, altering the received configuration for at least two iterations, calculating a first parameter of an altered configuration, calculating a second parameter of a consecutive altered configuration of the at least two iterations, comparing values of the calculated first parameter and second parameter, and determining a configuration having largest value of the calculated parameters.

Abstract: A computer-implemented method for detecting a characteristic in a sample of a set of samples is described. The method may include receiving from a user an indication for each sample of said set of samples that the user determines to include the characteristic. The method may also include defining samples of said set of samples that were not indicated by the user to include the characteristic as not including the characteristic. The method may further include iteratively applying by a processing unit, a detection algorithm on a first subset of the set of samples, said detection algorithm using a set of detection criteria that includes one or a plurality of detection criteria, evaluating a detection performance of the detection algorithm and modifying the detection algorithm by making changes in the set of detection criteria to enhance detection performance of the learning algorithm.

Abstract: A user trainable detecting apparatus for on site configuration comprises: one or more sensors; a detector for detecting events within the data arriving from the sensor, and a user interface that has labeling functionality, and which enables the user to label data from the sensor through the interface. A learning unit uses the labeled data for in-situ learning for use in the detector.