1 November 1991 Failure of outer-product learning to perform higher-order mapping
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Outer-product learning (also referred to as Hebbian learning) has been used as a very simple training algorithm for neural networks. Outer-product learning also has been proposed as a method for training a network with higher-order interconnections. It is shown in this paper that outer-product learning is inappropriate for higher-order networks because it does not have the ability to perform a non-monotonic mapping.
© (1991) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Jason M. Kinser, "Failure of outer-product learning to perform higher-order mapping", Proc. SPIE 1541, Infrared Sensors: Detectors, Electronics, and Signal Processing, (1 November 1991); doi: 10.1117/12.49333; https://doi.org/10.1117/12.49333

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