Abstract: One embodiment of the present invention provides a computer implemented method for generating a training set to train a convolutional neural network comprising the steps of providing prediction space data to a General Logic Gate Module (GLGM). Prediction space expert judgement is also provided to the GLGM and to a sensitivity and importance module. The GLGM determines or outputs state possibilities. The state possibilities are provided to the sensitivity and importance module and to the feature extraction module. Feature extraction algorithms are applied to the state possibilities within the feature extraction module to produce a training possibility set that is a virtual training possibility set. The training possibility set is provided to a state inferential module and to a final training set.
Abstract: One embodiment of the present invention provides a computer implemented method for generating a training set to train a convolutional neural network comprising the steps of providing prediction space data to a General Logic Gate Module (GLGM). Prediction space expert judgement is also provided to the GLGM and to a sensitivity and importance module. The GLGM determines or outputs state possibilities. The state possibilities are provided to the sensitivity and importance module and to the feature extraction module. Feature extraction algorithms are applied to the state possibilities within the feature extraction module to produce a training possibility set that is a virtual training possibility set. The training possibility set is provided to a state inferential module and to a final training set.