The EBNN Algorithm 4
{"Image DocumentName": "The EBNN Algorithm 4", "Image ImageDescription": "General case where the target function has multiple output units, the gradient is computed for each of these outputs => the matrix of the gradients called the Jacobian of the target function. Importance of the training derivatives: If the feature Expensive is irrelevant => has the value 0. Large positive or negative derivatives correspond to the assertion that the feature is highly relevant to determine the target value.", "Image XResolution": "37", "Image YResolution": "37", "Image ResolutionUnit": "Pixels/Centimeter", "Image Software": "SlidePlayer", "Image YCbCrPositioning": "Centered"}