Max B. Reid

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The authors describe a coarse coding technique and present simulation results illustrating its usefulness and its limitations. Simulations show that a third-order neural network can be trained to distinguish between two objects in a 4096x4096 pixel input field independent of transformations in translation, in-plane rotation, and scale in less than ten(More)
A nigher-order neural network (HONN) can be designed to be invariant to geometric transformations such as scale, translation, and in-plane rotation. Invariances are built directly into the arcnitecture of a HONN and do not need to be learned. Thus, for 2D object recognition, the network needs to be trained on just one View of each object class, not numerous(More)
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